diff --git a/.cnb.yml b/.cnb.yml new file mode 100644 index 00000000..3d82d6a4 --- /dev/null +++ b/.cnb.yml @@ -0,0 +1,24 @@ +# .cnb.yml +main: + push: + - imports: https://cnb.cool/tmacychen/sec/-/blob/main/envs.yml + stages: + - name: sync to github + image: tencentcom/git-sync + settings: + target_url: https://github.com/tmacychen/AKA-Sim.git + auth_type: https + username: $GIT_USERNAME + password: $GIT_ACCESS_TOKEN + +$: + push: + - imports: https://cnb.cool/tmacychen/sec/-/blob/main/envs.yml + stages: + - name: sync to github + image: tencentcom/git-sync + settings: + target_url: https://github.com/tmacychen/AKA-Sim.git + auth_type: https + username: $GIT_USERNAME + password: $GIT_ACCESS_TOKEN \ No newline at end of file diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..e86eed9f --- /dev/null +++ b/.dockerignore @@ -0,0 +1,13 @@ +__pycache__/ +.git/ +.idea/ +node_modules/ +static/ +output/ +checkpoints/ +*.pyc +*.pyo +.env +.DS_Store +.codebuddy/ +.github/ diff --git a/.gitignore b/.gitignore index b8cb34ec..6a38ba93 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,9 @@ __pycache__/ static output/ checkpoints/ +node_modules/ + +# Frontend build artifacts (auto-generated) +static-3d/assets/*.js +static-3d/assets/*.css +static-3d/index.html diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..353cd7ec --- /dev/null +++ b/Dockerfile @@ -0,0 +1,71 @@ +# ============================================================ +# AKA-Sim Dockerfile +# Multi-stage: dev (development) + prod (production) +# ============================================================ + +# ---------- Base ---------- +FROM python:3.11-slim AS base + +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && apt-get install -y --no-install-recommends \ + git \ + curl \ + wget \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Install Node.js 20 LTS +RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \ + && apt-get install -y --no-install-recommends nodejs \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Install Python dependencies first (layer cache) +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Install frontend dependencies +COPY frontend/package.json frontend/package-lock.json ./frontend/ +RUN cd frontend && npm ci + +# ---------- Development ---------- +FROM base AS dev + +# Install dev tools +RUN pip install --no-cache-dir \ + debugpy \ + watchdog \ + flake8 \ + black + +# Keep all source mounted, expose ports +# Port 80: Flask HTTP +# Port 5000: Flask fallback HTTP (Windows) +# Port 5173: Vite dev server +EXPOSE 80 5000 5173 + +CMD ["python", "run.py"] + +# ---------- Production ---------- +FROM base AS prod + +# Build frontend +COPY frontend/ ./frontend/ +RUN cd frontend && npm run build + +# Copy backend source +COPY backend/ ./backend/ +COPY run.py . +COPY https_init.sh . +COPY init.sh . +COPY main.html . +COPY templates/ ./templates/ + +# Copy built frontend to static folder +RUN cp -r frontend/dist/. static/ 2>/dev/null || mkdir -p static && cp -r frontend/dist/. static/ + +EXPOSE 80 443 + +CMD ["sh", "-c", "chmod +x /app/https_init.sh && /app/https_init.sh && python run.py"] diff --git a/README.md b/README.md index 12f5f903..452c6509 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,110 @@ AKA-Sim/ ## 快速开始 -### 1. 环境配置 +### 方式一：Docker（推荐） + +项目提供了多阶段 Docker 镜像，包含开发环境和生产环境两种构建目标。远程镜像已发布，可直接拉取使用，无需本地构建。 + +#### 环境要求 + +- Docker 20.10+ +- Docker Compose 2.0+（可选） + +#### 1. 拉取预构建镜像（推荐） + +跳过构建，直接拉取已有镜像： + +```bash +docker pull docker.cnb.cool/tmacychen/chenlongos-aka-sim:latest +docker tag docker.cnb.cool/tmacychen/chenlongos-aka-sim:latest aka-sim:dev +``` + +#### 2. 启动容器 + +```bash +# 启动开发容器（挂载本地源码） +docker run -d --name aka-sim-dev \ + -p 80:80 -p 5000:5000 \ + -v $(pwd):/app \ + -v /app/frontend/node_modules \ + -e PYTHONPATH=/app/backend \ + -w /app \ + aka-sim:dev + +# 首次启动需要构建前端静态资源 +docker exec aka-sim-dev bash -c "cd /app/frontend && npm run build" +``` + +访问 http://localhost:80 即可使用。 + +#### 3. 本地构建镜像 + +如需自定义构建，可使用以下命令： + +```bash +# 构建开发镜像 +docker build --target dev -t aka-sim:dev . + +# 构建生产镜像 +docker build --target prod -t aka-sim:prod . + +# 启动开发容器（挂载本地源码，支持热重载） +docker run -d --name aka-sim-dev \ + -p 80:80 -p 5000:5000 \ + -v $(pwd):/app \ + -v /app/frontend/node_modules \ + -e PYTHONPATH=/app/backend \ + -w /app \ + aka-sim:dev + +# 首次启动需要构建前端静态资源 +docker exec aka-sim-dev bash -c "cd /app/frontend && npm run build" + +# 启动生产容器 +docker run -d --name aka-sim-prod \ + -p 80:80 -p 443:443 \ + -e PYTHONPATH=/app/backend \ + aka-sim:prod +``` + +#### 使用 Docker Compose + +```bash +# 开发环境（源码挂载，支持热重载） +docker compose up dev + +# 生产环境（构建前端，仅运行时依赖） +docker compose up prod --build +``` + +#### 端口说明 + +| 端口 | 用途 | 说明 | +|------|------|------| +| 80 | Flask HTTP | 主服务端口 | +| 443 | Flask HTTPS | 生产环境，自动生成自签名证书 | +| 5000 | Flask HTTP 备用 | Windows 环境下的默认端口 | +| 5173 | Vite 开发服务器 | 仅开发环境 | + +#### Dockerfile 构建阶段 + +| 阶段 | 说明 | 基础镜像 | +|------|------|----------| +| `base` | 安装 Python 3.11、Node.js 20 及所有依赖 | python:3.11-slim | +| `dev` | 开发环境，额外安装 debugpy/watchdog/flake8/black | base | +| `prod` | 生产环境，自动构建 React 前端并复制到 static/ | base | + +#### GPU 支持（可选） + +如需 CUDA 加速，将 Dockerfile 基础镜像替换为： +```dockerfile +FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS base +``` +并安装对应版本的 PyTorch，运行时添加 `--gpus all` 参数。 + +### 方式二：本地安装 + +#### 1. 环境配置 创建 Python 3.11 环境： @@ -52,21 +155,27 @@ conda create -n aka-sim python=3.11 -y conda activate aka-sim ``` -### 2. 安装依赖 +#### 2. 安装依赖 ```bash pip install -r requirements.txt ``` -### 3. 启动后端服务 +#### 3. 构建前端（React） + +```bash +cd frontend && npm ci && npm run build && cd .. +``` + +#### 4. 启动后端服务 ```bash -python run.py +PYTHONPATH=backend python run.py ``` -后端启动后，将main.html拖到浏览器中打开前端页面： +#### 5. 访问服务 -服务启动后访问： +- http://localhost/ - 首页 - http://localhost/sim - 模拟器 ## API 接口 diff --git a/backend/app/routes/api.py b/backend/app/routes/api.py index 729e0843..1648422d 100644 --- a/backend/app/routes/api.py +++ b/backend/app/routes/api.py @@ -59,6 +59,10 @@ "env_state_dim": 0, "action_dim": 0, "chunk_size": 0, + # Action chunk caching for temporal ensembling + "chunk_buffer": [], # list of past action chunks for ensembling + "chunk_index": 0, # current step index within the cached chunk + "current_chunk": None, # (chunk_size, action_dim) tensor } @@ -107,10 +111,18 @@ def save_dataset(): return jsonify({"error": "invalid images"}), 400 if len(images) != len(actions): return jsonify({"error": "images length mismatch"}), 400 + def _sanitize_float_list(data): + """Recursively replace None/NaN/Inf with 0.0 to prevent tensor creation errors.""" + if isinstance(data, list): + return [_sanitize_float_list(item) for item in data] + if data is None or (isinstance(data, float) and (math.isnan(data) or math.isinf(data))): + return 0.0 + return data + dataset = { - OBS_STATE: torch.tensor(states, dtype=torch.float32), - OBS_ENV_STATE: torch.tensor(env_states, dtype=torch.float32), - ACTION: torch.tensor(actions, dtype=torch.float32), + OBS_STATE: torch.tensor(_sanitize_float_list(states), dtype=torch.float32), + OBS_ENV_STATE: torch.tensor(_sanitize_float_list(env_states), dtype=torch.float32), + ACTION: torch.tensor(_sanitize_float_list(actions), dtype=torch.float32), "action_is_pad": torch.tensor(action_is_pad, dtype=torch.bool), } if rewards is not None: @@ -239,47 +251,23 @@ def _list_models(): return models -def _map_action(action_vector, action_dim: int): - if action_dim >= 5: - idx = int(max(range(5), key=lambda i: action_vector[i])) - return ["up", "down", "left", "right", "stop"][idx] - if action_dim == 4: - idx = int(max(range(4), key=lambda i: action_vector[i])) - return ["up", "down", "left", "right"][idx] - if action_dim == 3: - idx = int(max(range(3), key=lambda i: action_vector[i])) - return ["up", "down", "stop"][idx] - if action_dim == 2: - move = float(action_vector[0]) - turn = float(action_vector[1]) - if abs(move) >= abs(turn): - return "up" if move >= 0 else "down" - return "right" if turn >= 0 else "left" - if action_dim == 1: - return "up" if float(action_vector[0]) >= 0 else "down" - return "stop" - - -def _apply_action(action: str): - if action == "up": - if car.speed < car.maxSpeed: - car.speed += car.acceleration - if action == "down": - if car.speed > -car.maxSpeed / 2: - car.speed -= car.acceleration - if action == "left": - car.angle -= car.rotationSpeed - if action == "right": - car.angle += car.rotationSpeed +def _apply_continuous_action(action_vec, action_dim: int): + """Apply continuous action [velocity, angular_velocity] to car physics.""" + velocity = float(action_vec[0]) + angular_vel = float(action_vec[1]) if action_dim >= 2 else 0.0 + + # Clamp values + velocity = max(-0.15, min(0.15, velocity)) + angular_vel = max(-0.08, min(0.08, angular_vel)) + car.speed += velocity + car.angle += angular_vel + + # Friction car.speed *= car.friction car.x += math.cos(car.angle) * car.speed car.y += math.sin(car.angle) * car.speed - if action == "stop": - car.x -= math.cos(car.angle) * car.speed * 2 - car.y -= math.sin(car.angle) * car.speed * 2 - car.speed = 0 state = car.get_state() socketio.emit("car_state", state) @@ -538,6 +526,7 @@ def infer_step(): state_dim = infer_state["state_dim"] env_state_dim = infer_state["env_state_dim"] action_dim = infer_state["action_dim"] + chunk_size = infer_state["chunk_size"] def _to_fixed_vec(raw, dim: int): vec = [0.0] * dim @@ -553,6 +542,7 @@ def _to_fixed_vec(raw, dim: int): data = request.get_json(silent=True) or {} state_input = data.get("state") env_input = data.get("env_state") + force_replan = data.get("force_replan", False) if state_input is None or env_input is None: state = car.get_state() @@ -565,14 +555,47 @@ def _to_fixed_vec(raw, dim: int): state_vec = _to_fixed_vec(state_input, state_dim) env_vec = _to_fixed_vec(env_input, env_state_dim) - state_tensor = torch.tensor([state_vec], dtype=torch.float32, device=device) - env_tensor = torch.tensor([env_vec], dtype=torch.float32, device=device) - with torch.no_grad(): - actions, _ = model({OBS_STATE: state_tensor, OBS_ENV_STATE: env_tensor}) - action_vec = actions[0, 0].detach().cpu().numpy().tolist() - action = _map_action(action_vec, action_dim) - _apply_action(action) - return jsonify({"status": "ok", "action": action, "action_vector": action_vec}) + with infer_lock: + chunk_idx = infer_state["chunk_index"] + current_chunk = infer_state["current_chunk"] + chunk_buffer = infer_state["chunk_buffer"] + + # Run model forward pass to get new action chunk + state_tensor = torch.tensor([state_vec], dtype=torch.float32, device=device) + env_tensor = torch.tensor([env_vec], dtype=torch.float32, device=device) + with torch.no_grad(): + actions, _ = model({OBS_STATE: state_tensor, OBS_ENV_STATE: env_tensor}) + # actions shape: (1, chunk_size, action_dim) + new_chunk = actions[0].detach().cpu() # (chunk_size, action_dim) + + # Temporal ensembling: average with past chunks using exponential decay + chunk_buffer.append(new_chunk) + if len(chunk_buffer) > chunk_size: + chunk_buffer.pop(0) + + if len(chunk_buffer) >= 2: + ensembled = torch.zeros_like(new_chunk) + total_weight = 0.0 + for i, past_chunk in enumerate(chunk_buffer): + weight = 0.5 ** (len(chunk_buffer) - 1 - i) # exponential decay + ensembled += past_chunk * weight + total_weight += weight + ensembled /= total_weight + else: + ensembled = new_chunk + + # Cache the ensembled chunk and use step-by-step + infer_state["current_chunk"] = ensembled + infer_state["chunk_index"] = 0 + action_vec = ensembled[0].numpy().tolist() + + # Return action only - backend does not apply physics (frontend handles it) + return jsonify({ + "status": "ok", + "action": action_vec, + "action_dim": action_dim, + "chunk_size": chunk_size, + }) @api_bp.route("/infer/stop", methods=["POST"]) @@ -589,6 +612,9 @@ def infer_stop(): "env_state_dim": 0, "action_dim": 0, "chunk_size": 0, + "chunk_buffer": [], + "chunk_index": 0, + "current_chunk": None, } ) return jsonify({"status": "stopped"}) diff --git a/backend/app/routes/frontend.py b/backend/app/routes/frontend.py index c3b0b368..d4155660 100644 --- a/backend/app/routes/frontend.py +++ b/backend/app/routes/frontend.py @@ -1,10 +1,66 @@ -from flask import Blueprint, render_template +import os +from flask import Blueprint, send_file frontend_bp = Blueprint("frontend", __name__) -@frontend_bp.route("/", defaults={"path": ""}) -@frontend_bp.route("/") -def serve_react(path): - if path.startswith("api"): - return {"error": "Not Found"}, 404 - return render_template("index.html") +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + +STATIC_DIR = os.path.join(ROOT_DIR, "static") +STATIC_3D_DIR = os.path.join(ROOT_DIR, "static-3d") + +MIME_TYPES = { + '.html': 'text/html; charset=utf-8', + '.js': 'application/javascript; charset=utf-8', + '.css': 'text/css; charset=utf-8', + '.svg': 'image/svg+xml', + '.png': 'image/png', + '.ico': 'image/x-icon', + '.json': 'application/json; charset=utf-8', + '.wasm': 'application/wasm', +} + +def _send_static(file_path): + """发送静态文件，自动设置 MIME 类型""" + if not os.path.isfile(file_path): + return None + ext = os.path.splitext(file_path)[1].lower() + mime = MIME_TYPES.get(ext) + if mime: + return send_file(file_path, mimetype=mime) + return send_file(file_path) + +@frontend_bp.route("/") +def serve_control(): + """控制台页面，由 frontend/control.html 构建而来""" + index_html = os.path.join(STATIC_DIR, "control.html") + result = _send_static(index_html) + if result: + return result + return {"error": "Control page not built. Run: cd frontend && npm run build:control"}, 404 + +@frontend_bp.route("/assets/") +def serve_control_assets(path): + """控制台页面静态资源""" + file_path = os.path.join(STATIC_DIR, "assets", path) + result = _send_static(file_path) + if result: + return result + return {"error": "Not Found"}, 404 + +@frontend_bp.route("/sim3d") +def serve_sim3d(): + """3D 模拟器页面，使用 Vite 构建的 3D 前端""" + index_html = os.path.join(STATIC_3D_DIR, "index.html") + result = _send_static(index_html) + if result: + return result + return {"error": "3D frontend not built. Run: cd frontend && npm install && npm run build"}, 404 + +@frontend_bp.route("/sim3d/") +def serve_sim3d_assets(path): + """3D 前端静态资源""" + file_path = os.path.join(STATIC_3D_DIR, path) + result = _send_static(file_path) + if result: + return result + return {"error": "Not Found"}, 404 diff --git a/backend/app/routes/websocket.py b/backend/app/routes/websocket.py index 0fe1ca27..ff9d4e0f 100644 --- a/backend/app/routes/websocket.py +++ b/backend/app/routes/websocket.py @@ -57,17 +57,28 @@ def _load_act_model(): if _act_model is not None: return _act_device = "cuda" if torch.cuda.is_available() else "cpu" - state_dim = int(os.getenv("ACT_STATE_DIM", "14")) - env_state_dim = int(os.getenv("ACT_ENV_STATE_DIM", "6")) - action_dim = int(os.getenv("ACT_ACTION_DIM", "7")) - chunk_size = int(os.getenv("ACT_CHUNK_SIZE", "16")) checkpoint_path = os.getenv( "ACT_CHECKPOINT_PATH", os.path.join("output", "train", "act_demo", "act_checkpoint.pt"), ) + # Try to load dimensions from checkpoint first + try: + checkpoint = torch.load(checkpoint_path, map_location=_act_device) + state_dim = int(checkpoint.get("state_dim", 14)) + env_state_dim = int(checkpoint.get("env_state_dim", 10)) + action_dim = int(checkpoint.get("action_dim", 2)) + chunk_size = int(checkpoint.get("chunk_size", 10)) + use_vae = bool(checkpoint.get("use_vae", False)) + except Exception: + # Fallback to env vars + state_dim = int(os.getenv("ACT_STATE_DIM", "14")) + env_state_dim = int(os.getenv("ACT_ENV_STATE_DIM", "10")) + action_dim = int(os.getenv("ACT_ACTION_DIM", "2")) + chunk_size = int(os.getenv("ACT_CHUNK_SIZE", "10")) + use_vae = False config = ACTConfig( chunk_size=chunk_size, - use_vae=False, + use_vae=use_vae, input_features={ OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,)), OBS_ENV_STATE: PolicyFeature(type=FeatureType.ENV, shape=(env_state_dim,)), diff --git a/demo.html b/demo.html new file mode 100644 index 00000000..fdd19054 --- /dev/null +++ b/demo.html @@ -0,0 +1,1158 @@ + + + + + + LeRobot ACT 模型教学模拟器 + + + + + + + + +
+
+
🤖
+
+

LeRobot ACT Simulator

+

Action Chunking with Transformers - Educational Edition

+
+
+
+
+ + Simulation Active +
+
60 FPS
+
+
+ + +
+ + + + + +
+
+ + +
+
Position: 0.00, 0.00
+
Rotation: 0°
+
Velocity: 0.0 m/s
+
+ + +
+ 手动控制模式 +
+
+ + + +
+ + + + diff --git a/demo2.html b/demo2.html new file mode 100644 index 00000000..315c022a --- /dev/null +++ b/demo2.html @@ -0,0 +1,1239 @@ + + + + + + LeRobot ACT Simulator (Vanilla JS) + + + + + + + + + +
+
+
🤖
+
+

LeRobot ACT Simulator

+

Action Chunking with Transformers - Vanilla JS Edition

+
+
+
+
+ + Simulation Active +
+
60 FPS
+
+
+ +
+ + + + +
+
+ +
+
Position: 0.00, 0.00
+
Rotation: 0°
+
Velocity: 0.0 m/s
+
+ +
+ 手动控制模式 +
+
+ + + +
+ + + + diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..601d7001 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,33 @@ +services: + # -------- Development -------- + dev: + build: + context: . + target: dev + container_name: aka-sim-dev + ports: + - "80:80" + - "5000:5000" + - "5173:5173" # Vite dev server + volumes: + - .:/app # Mount source code for hot reload + - /app/frontend/node_modules # Prevent host/node_modules conflict + environment: + - FLASK_DEBUG=1 + - APP_HTTP_PORT=5000 + - PYTHONPATH=/app/backend + command: python run.py + + # -------- Production -------- + prod: + build: + context: . + target: prod + container_name: aka-sim-prod + ports: + - "80:80" + - "443:443" + environment: + - APP_HTTP_PORT=80 + - APP_HTTPS_PORT=443 + restart: unless-stopped diff --git a/frontend/.gitignore b/frontend-old/.gitignore similarity index 100% rename from frontend/.gitignore rename to frontend-old/.gitignore diff --git a/frontend/README.md b/frontend-old/README.md similarity index 100% rename from frontend/README.md rename to frontend-old/README.md diff --git a/frontend/eslint.config.js b/frontend-old/eslint.config.js similarity index 100% rename from frontend/eslint.config.js rename to frontend-old/eslint.config.js diff --git a/frontend-old/index.html b/frontend-old/index.html new file mode 100644 index 00000000..cacc7c37 --- /dev/null +++ b/frontend-old/index.html @@ -0,0 +1,13 @@ + + + + + + + frontend + + +
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"^9.39.1", + "@types/node": "^24.10.1", + "@types/react": "^19.2.7", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "typescript": "~5.9.3", + "typescript-eslint": "^8.48.0", + "vite": "^8.0.0-beta.13" + }, + "overrides": { + "vite": "^8.0.0-beta.13" + } +} diff --git a/frontend/public/vite.svg b/frontend-old/public/vite.svg similarity index 100% rename from frontend/public/vite.svg rename to frontend-old/public/vite.svg diff --git a/frontend/src/App.css b/frontend-old/src/App.css similarity index 100% rename from frontend/src/App.css rename to frontend-old/src/App.css diff --git a/frontend-old/src/App.tsx b/frontend-old/src/App.tsx new file mode 100644 index 00000000..8fd272b1 --- /dev/null +++ b/frontend-old/src/App.tsx @@ -0,0 +1,14 @@ +import {Route, Routes} from "react-router-dom"; +import BaseControlPage from "./pages/BaseControlPage.tsx"; +import SimPage from "./pages/SimPage.tsx"; + +function App() { + return ( + + }/> + }/> + + ) +} + +export default App; diff --git a/frontend-old/src/api/socket.ts b/frontend-old/src/api/socket.ts new file mode 100644 index 00000000..a1f4d4b8 --- /dev/null +++ b/frontend-old/src/api/socket.ts @@ -0,0 +1,36 @@ +import {io} from "socket.io-client"; + +export const socket = io({ + path: "/socket.io", + transports: ["websocket", "polling"], + upgrade: true, + reconnection: true, + reconnectionAttempts: Infinity, + reconnectionDelay: 500, + timeout: 20000 +}); + +export const sendAction = (action: string) => { + socket.emit('action', action); +} + +export const resetCar = () => { + socket.emit('reset_car_state'); +} + +export const getCarState = () => { + socket.emit('get_car_state'); +} + +export const actInfer = (payload: Record) => { + socket.emit('act_infer', payload); +} + +export const saveDataset = async (payload: Record) => { + const res = await fetch(`/api/dataset`, { + method: 'POST', + headers: {'Content-Type': 'application/json'}, + body: JSON.stringify(payload) + }) + return res.json() +} diff --git a/frontend/src/assets/react.svg b/frontend-old/src/assets/react.svg similarity index 100% rename from frontend/src/assets/react.svg rename to frontend-old/src/assets/react.svg diff --git a/frontend-old/src/components/ControlButton.tsx b/frontend-old/src/components/ControlButton.tsx new file mode 100644 index 00000000..faef0602 --- /dev/null +++ b/frontend-old/src/components/ControlButton.tsx @@ -0,0 +1,80 @@ +import {useState} from "react"; + +type ButtonProps = { + children: React.ReactNode; + onPressStart?: () => void; + onPressEnd?: () => void; + onClick?: () => void; + variant?: "primary" | "danger" | "success" | "secondary"; + size?: "square" | "wide"; +}; + +const colorMap = { + primary: "#2563eb", + danger: "#ef4444", + success: "#22c55e", + secondary: "#1e293b", +}; + +const pressedColorMap = { + primary: "#1d4ed8", + danger: "#dc2626", + success: "#16a34a", + secondary: "#0f172a", +}; + +const ControlButton = ({ + children, + variant = "primary", + size = "square", + onPressStart, + onPressEnd, + onClick, + }: ButtonProps) => { + const [isPressed, setIsPressed] = useState(false); + const baseSize = + size === "square" + ? {width: "22vw", height: "22vw", maxWidth: "110px", maxHeight: "110px"} + : {width: "40vw", height: "60px", maxWidth: "200px"}; + + const handlePressStart = () => { + setIsPressed(true); + onPressStart?.(); + }; + + const handlePressEnd = () => { + setIsPressed(false); + onPressEnd?.(); + }; + + return ( + + ) +} + +export default ControlButton \ No newline at end of file diff --git a/frontend/src/components/target/ImportPreviewPanel.tsx b/frontend-old/src/components/target/ImportPreviewPanel.tsx similarity index 100% rename from frontend/src/components/target/ImportPreviewPanel.tsx rename to frontend-old/src/components/target/ImportPreviewPanel.tsx diff --git a/frontend/src/components/target/ModalImportDialog.tsx b/frontend-old/src/components/target/ModalImportDialog.tsx similarity index 100% rename from frontend/src/components/target/ModalImportDialog.tsx rename to frontend-old/src/components/target/ModalImportDialog.tsx diff --git a/frontend/src/components/target/TargetManager.tsx b/frontend-old/src/components/target/TargetManager.tsx similarity index 100% rename from frontend/src/components/target/TargetManager.tsx rename to frontend-old/src/components/target/TargetManager.tsx diff --git a/frontend/src/components/target/TargetRenderer.tsx b/frontend-old/src/components/target/TargetRenderer.tsx similarity index 100% rename from frontend/src/components/target/TargetRenderer.tsx rename to frontend-old/src/components/target/TargetRenderer.tsx diff --git a/frontend/src/components/target/useTargetCreation.ts b/frontend-old/src/components/target/useTargetCreation.ts similarity index 100% rename from frontend/src/components/target/useTargetCreation.ts rename to frontend-old/src/components/target/useTargetCreation.ts diff --git a/frontend/src/components/target/useTargetDrag.ts b/frontend-old/src/components/target/useTargetDrag.ts similarity index 100% rename from frontend/src/components/target/useTargetDrag.ts rename to frontend-old/src/components/target/useTargetDrag.ts diff --git a/frontend-old/src/index.css b/frontend-old/src/index.css new file mode 100644 index 00000000..5f0b13af --- /dev/null +++ b/frontend-old/src/index.css @@ -0,0 +1,79 @@ +:root { + font-family: system-ui, Avenir, Helvetica, Arial, sans-serif; + line-height: 1.5; + font-weight: 400; + + color-scheme: light dark; + color: rgba(255, 255, 255, 0.87); + background-color: #242424; + + font-synthesis: none; + text-rendering: optimizeLegibility; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + + -webkit-touch-callout: none !important; /* 禁用 iOS 长按弹出“拷贝” */ + -webkit-user-select: none !important; /* 禁用文本选择 */ + -moz-user-select: none !important; + -ms-user-select: none !important; + user-select: none !important; + + -webkit-tap-highlight-color: transparent !important; /* 去掉点击高亮灰块（iOS/Android）*/ + -webkit-focus-ring-color: transparent !important; + outline: none !important; +} + +a { + font-weight: 500; + color: #646cff; + text-decoration: inherit; +} + +a:hover { + color: #535bf2; +} + +body { + +} + +h1 { + font-size: 3.2em; + line-height: 1.1; +} + +button { + border-radius: 8px; + border: 1px solid transparent; + padding: 0.6em 1.2em; + font-size: 1em; + font-weight: 500; + font-family: inherit; + background-color: #1a1a1a; + cursor: pointer; + transition: border-color 0.25s; +} + +button:hover { + border-color: #646cff; +} + +button:focus, +button:focus-visible { + outline: 4px auto -webkit-focus-ring-color; +} + +@media (prefers-color-scheme: light) { + :root { + color: #213547; + background-color: #ffffff; + } + + a:hover { + color: #747bff; + } + + button { + background-color: #f9f9f9; + } +} diff --git a/frontend-old/src/main.tsx b/frontend-old/src/main.tsx new file mode 100644 index 00000000..fd2cc0a0 --- /dev/null +++ b/frontend-old/src/main.tsx @@ -0,0 +1,10 @@ +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.tsx' +import {BrowserRouter} from "react-router-dom"; + +createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/frontend/src/model/car.ts b/frontend-old/src/model/car.ts similarity index 100% rename from frontend/src/model/car.ts rename to frontend-old/src/model/car.ts diff --git a/frontend/src/model/target.ts b/frontend-old/src/model/target.ts similarity index 100% rename from frontend/src/model/target.ts rename to frontend-old/src/model/target.ts diff --git a/frontend-old/src/pages/BaseControlPage.tsx b/frontend-old/src/pages/BaseControlPage.tsx new file mode 100644 index 00000000..b704021e --- /dev/null +++ b/frontend-old/src/pages/BaseControlPage.tsx @@ -0,0 +1,303 @@ +import {useEffect, useRef, useState} from "react"; +import {sendAction} from "../api/socket.ts"; +import ControlButton from "../components/ControlButton.tsx"; + +const FPS = 20 +const frameInterval = 1000 / FPS + +const BaseControlPage = () => { + const [ip, setIp] = useState("获取中..."); + const [status, setStatus] = useState("准备就绪"); + const [isSimulator, setIsSimulator] = useState(false); + const [targetIp, setTargetIp] = useState(""); + + // 当前正在执行的动作（用于模拟器每帧发送） + const currentActionRef = useRef(null); + + useEffect(() => { + const parseJsonSafe = async (res: Response) => { + const text = await res.text(); + if (!text) return null; + try { + return JSON.parse(text); + } catch { + return null; + } + }; + + const getIp = async () => { + setStatus("获取 IP..."); + try { + const res = await fetch("/api/ip"); + if (!res.ok) throw new Error("请求失败"); + const data = await parseJsonSafe(res); + if (!data?.ip) throw new Error("无IP数据"); + console.log("device ip:", data.ip); + setIp("IP: " + data.ip); + setStatus("准备就绪"); + } catch { + setStatus("获取 IP 失败"); + } + }; + + getIp(); + }, []); + + const send = async (action: string) => { + setStatus("执行: " + action); + if (!isSimulator) { + console.log("http send " + action); + if (!targetIp) { + setStatus("请输入目标IP"); + return; + } + try { + const res = await fetch(`/api/control?action=${action}&speed=50&time=0&target_ip=${encodeURIComponent(targetIp)}`); + if (!res.ok) throw new Error("请求失败"); + const text = await res.text(); + if (text) { + try { + console.log(JSON.parse(text)); + } catch { + console.log(text); + } + } + } catch (err) { + setStatus("错误: " + err); + } + } + }; + + // ==== 按钮事件处理 ==== + const handlePressStart = (action: string) => { + currentActionRef.current = action; + if (!isSimulator) { + send(action); // 实车立即发 + } + }; + + const handlePressEnd = () => { + currentActionRef.current = null; + if (!isSimulator) { + send("stop"); // 实车发 stop + } + }; + + useEffect(() => { + if (!isSimulator) return; // 只在模拟器模式运行 + let animationFrameId: number + let lastTime = 0; + const renderLoop = (currentTime: number) => { + animationFrameId = window.requestAnimationFrame(renderLoop) + const action = currentActionRef.current; + const delta = currentTime - lastTime + + if (delta < frameInterval) return + + lastTime = currentTime - (delta % frameInterval) + + if (action !== null) { + sendAction(action); // 每帧发送当前动作 + } + }; + + animationFrameId = requestAnimationFrame(renderLoop); + + return () => { + window.cancelAnimationFrame(animationFrameId) + } + + }, [isSimulator]) + + const redirect = async () => { + setStatus("获取 IP..."); + try { + const res = await fetch("/api/ip"); + if (!res.ok) throw new Error("请求失败"); + const text = await res.text(); + if (!text) throw new Error("空响应"); + const data = JSON.parse(text); + if (!data?.ip) throw new Error("无IP数据"); + const targetUrl = "https://ai.maodouketang.cn/"; + const fullUrl = `${targetUrl}?ip=${encodeURIComponent(data.ip)}`; + window.location.replace(fullUrl); + } catch (err) { + console.error("跳转失败:", err); + setStatus("跳转失败"); + alert("无法获取IP，请稍后重试"); + } + }; + + return ( +
+

AKA-00 控制台

+
{ip}
+ + {/* 模式状态 */} +
+ 模式： + + {isSimulator ? "模拟" : "实车"} + +
+ + {/* 目标IP输入 */} + {!isSimulator && ( +
+ setTargetIp(e.target.value)} + style={{ + padding: "8px 12px", + borderRadius: "8px", + border: "1px solid #3b82f6", + background: "#1e293b", + color: "white", + fontSize: "14px", + width: "200px", + textAlign: "center", + }} + /> +
+ )} + + {/* 方向区 */} +
+
+ handlePressStart("up")} + onPressEnd={() => handlePressEnd()} + > + 前进 + +
+ handlePressStart("left")} + onPressEnd={() => handlePressEnd()} + > + 左转 + + handlePressStart("stop")} + onPressEnd={() => handlePressEnd()} + > + 停止 + + handlePressStart("right")} + onPressEnd={() => handlePressEnd()} + > + 右转 + +
+ + handlePressStart("down")} + onPressEnd={() => handlePressEnd()} + > + 后退 + +
+
+ + {/* 功能按钮 */} +
+ send("grab")} + > + 抓取 + + + send("release")} + > + 释放 + +
+ +
+ {/* 跳转 */} +
+ redirect()} + > + 进入试验平台 + +
+ + {/* 切换 */} +
+ setIsSimulator(!isSimulator)} + > + 切换模式 + +
+
+ +
+ {status} +
+
+ ); +} + +export default BaseControlPage; diff --git a/frontend/src/pages/SimPage.tsx b/frontend-old/src/pages/SimPage.tsx similarity index 100% rename from frontend/src/pages/SimPage.tsx rename to frontend-old/src/pages/SimPage.tsx diff --git a/frontend/src/store/targetStore.ts b/frontend-old/src/store/targetStore.ts similarity index 100% rename from frontend/src/store/targetStore.ts rename to frontend-old/src/store/targetStore.ts diff --git a/frontend/tsconfig.app.json b/frontend-old/tsconfig.app.json similarity index 100% rename from frontend/tsconfig.app.json rename to frontend-old/tsconfig.app.json diff --git a/frontend-old/tsconfig.json b/frontend-old/tsconfig.json new file mode 100644 index 00000000..1ffef600 --- /dev/null +++ b/frontend-old/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend-old/tsconfig.node.json similarity index 100% rename from frontend/tsconfig.node.json rename to frontend-old/tsconfig.node.json diff --git a/frontend-old/vite.config.ts b/frontend-old/vite.config.ts new file mode 100644 index 00000000..af7e68cd --- /dev/null +++ b/frontend-old/vite.config.ts @@ -0,0 +1,32 @@ +import {defineConfig} from 'vite' +import react from '@vitejs/plugin-react' +import path from 'path' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], + server: { + proxy: { + "/api": { + target: "http://localhost:5000", + changeOrigin: true, + }, + "/socket.io": { + target: "http://localhost:5000", + ws: true, + changeOrigin: true, + }, + }, + }, + build: { + outDir: path.resolve(__dirname, '../static'), + emptyOutDir: true, + rolldownOptions: { + output: { + entryFileNames: `assets/[name].js`, + chunkFileNames: `assets/[name].js`, + assetFileNames: `assets/[name].[ext]` + } + } + } +}) diff --git a/frontend/.env.example b/frontend/.env.example new file mode 100644 index 00000000..7a550fe7 --- /dev/null +++ b/frontend/.env.example @@ -0,0 +1,9 @@ +# GEMINI_API_KEY: Required for Gemini AI API calls. +# AI Studio automatically injects this at runtime from user secrets. +# Users configure this via the Secrets panel in the AI Studio UI. +GEMINI_API_KEY="MY_GEMINI_API_KEY" + +# APP_URL: The URL where this applet is hosted. +# AI Studio automatically injects this at runtime with the Cloud Run service URL. +# Used for self-referential links, OAuth callbacks, and API endpoints. +APP_URL="MY_APP_URL" diff --git a/frontend/control.html b/frontend/control.html new file mode 100644 index 00000000..5af81eb4 --- /dev/null +++ b/frontend/control.html @@ -0,0 +1,12 @@ + + + + + + AKA-00 控制台 + + +
+ + + diff --git a/frontend/index.html b/frontend/index.html index cacc7c37..5fe11357 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -2,12 +2,14 @@ - - frontend + + + AKA ACT Simulator
- + + diff --git a/frontend/metadata.json b/frontend/metadata.json new file mode 100644 index 00000000..c148fc40 --- /dev/null +++ b/frontend/metadata.json @@ -0,0 +1,5 @@ +{ + "name": "LeRobot ACT Simulator", + "description": "A 3D simulator for teaching LeRobot ACT model training with data collection and inference.", + "requestFramePermissions": [] +} diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 14723f1f..b5b269dc 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,39 +1,44 @@ { - "name": "frontend", + "name": "react-example", "version": "0.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { - "name": "frontend", + "name": "react-example", "version": "0.0.0", "dependencies": { - "react": "^19.2.0", - "react-dom": "^19.2.0", - "react-router-dom": "^7.13.0", + "@google/genai": "^1.29.0", + "@tailwindcss/vite": "^4.1.14", + "@tensorflow/tfjs": "^4.22.0", + "@vitejs/plugin-react": "^5.0.4", + "better-sqlite3": "^12.4.1", + "dotenv": "^17.2.3", + "express": "^4.21.2", + "jszip": "^3.10.1", + "lucide-react": "^0.546.0", + "motion": "^12.23.24", + "react": "^19.0.0", + "react-dom": "^19.0.0", "socket.io-client": "^4.8.3", - "zustand": "^5.0.11" + "three": "^0.183.2", + "vite": "^6.2.0" }, "devDependencies": { - "@eslint/js": "^9.39.1", - "@types/node": "^24.10.1", - "@types/react": "^19.2.7", - "@types/react-dom": "^19.2.3", - "@vitejs/plugin-react": "^5.1.1", - "eslint": "^9.39.1", - "eslint-plugin-react-hooks": "^7.0.1", - "eslint-plugin-react-refresh": "^0.4.24", - "globals": "^16.5.0", - "typescript": "~5.9.3", - "typescript-eslint": "^8.48.0", - "vite": "^8.0.0-beta.13" + "@types/express": "^4.17.21", + "@types/node": "^22.14.0", + "autoprefixer": "^10.4.21", + "gh-pages": "^6.1.1", + "tailwindcss": "^4.1.14", + "tsx": "^4.21.0", + "typescript": "~5.8.2", + "vite": "^6.2.0" } }, "node_modules/@babel/code-frame": { "version": "7.29.0", "resolved": 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"lint": "eslint .", - "preview": "vite preview" + "dev": "vite --port=3000 --host=0.0.0.0", + "build": "vite build", + "build:control": "vite build --config vite.config.control.ts", + "build:all": "vite build && vite build --config vite.config.control.ts", + "preview": "vite preview", + "clean": "rm -rf dist", + "lint": "tsc --noEmit", + "deploy": "gh-pages -d dist", + "predeploy": "npm run build" }, "dependencies": { - "react": "^19.2.0", - "react-dom": "^19.2.0", - "react-router-dom": "^7.13.0", + "@google/genai": "^1.29.0", + "@tailwindcss/vite": "^4.1.14", + "@tensorflow/tfjs": "^4.22.0", + "@vitejs/plugin-react": "^5.0.4", + "better-sqlite3": "^12.4.1", + "dotenv": "^17.2.3", + "express": "^4.21.2", + "jszip": "^3.10.1", + "lucide-react": "^0.546.0", + "motion": "^12.23.24", + "react": "^19.0.0", + "react-dom": "^19.0.0", "socket.io-client": "^4.8.3", - "zustand": "^5.0.11" + "three": "^0.183.2", + "vite": "^6.2.0" }, "devDependencies": { - "@eslint/js": "^9.39.1", - "@types/node": "^24.10.1", - "@types/react": "^19.2.7", - "@types/react-dom": "^19.2.3", - "@vitejs/plugin-react": "^5.1.1", - "eslint": "^9.39.1", - "eslint-plugin-react-hooks": "^7.0.1", - "eslint-plugin-react-refresh": "^0.4.24", - "globals": "^16.5.0", - "typescript": "~5.9.3", - "typescript-eslint": "^8.48.0", - "vite": "^8.0.0-beta.13" - }, - "overrides": { - "vite": "^8.0.0-beta.13" + "@types/express": "^4.17.21", + "@types/node": "^22.14.0", + "autoprefixer": "^10.4.21", + "gh-pages": "^6.1.1", + "tailwindcss": "^4.1.14", + "tsx": "^4.21.0", + "typescript": "~5.8.2", + "vite": "^6.2.0" } -} +} \ No newline at end of file diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 8fd272b1..e78e5283 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -1,14 +1,2788 @@ -import {Route, Routes} from "react-router-dom"; -import BaseControlPage from "./pages/BaseControlPage.tsx"; -import SimPage from "./pages/SimPage.tsx"; +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import * as THREE from 'three'; +import * as tf from '@tensorflow/tfjs'; +import { cloudService } from './services/cloudService'; +import { actService } from './services/actService'; +import { CloudModel, CloudDataset, CloudTrainingStatus, SimulationState, RobotConfig, SceneType, SceneSize, SceneComplexity, LogEntry } from './types'; +import { createScene, createCamera, createRenderer, createLights, createFloor } from './sim/scene'; +import { createRobot } from './sim/robot'; +import { updateEnvironment } from './sim/environment'; +import { tr } from 'motion/react-client'; -function App() { - return ( - - }/> - }/> - - ) +interface Obstacle { + id: string; + x: number; + z: number; + w: number; + h: number; + d: number; + color: number; + type: 'box' | 'cylinder'; + rotation: number; +} + +interface ScenePreset { + id: string; + name: string; + sceneType: SceneType; + sceneSize: SceneSize; + sceneComplexity: SceneComplexity; + obstacles: Omit[]; + targetPos: { x: number; z: number }; + createdAt: string; } -export default App; +const SCENE_PRESETS_KEY = 'scenePresets'; + +let obstacleIdCounter = 0; +const nextObstacleId = () => `obs_${++obstacleIdCounter}_${Date.now()}`; + +export default function App() { + // State + const [isRecording, setIsRecording] = useState(false); + const [isTraining, setIsTraining] = useState(false); + const [isInferencing, setIsInferencing] = useState(false); + const [episodesCount, setEpisodesCount] = useState(0); + const [frameCount, setFrameCount] = useState(0); + const [actionCount, setActionCount] = useState(0); + const [trainingProgress, setTrainingProgress] = useState(0); + const [trainingStatus, setTrainingStatus] = useState(''); + const [trainedModels, setTrainedModels] = useState<{ name: string }[]>([ + { name: '跟随网球示例' }, + { name: '自动避障示例' } + ]); + const [selectedModel, setSelectedModel] = useState('跟随网球示例'); + const [trainedModel, setTrainedModel] = useState<{ name: string } | null>(null); + const [showAttention, setShowAttention] = useState(false); + const [sceneType, setSceneType] = useState(() => localStorage.getItem('sceneType') || 'basic'); + const [sceneSize, setSceneSize] = useState(() => localStorage.getItem('sceneSize') || 'medium'); + const [sceneComplexity, setSceneComplexity] = useState(() => localStorage.getItem('sceneComplexity') || 'low'); + const [hasArm, setHasArm] = useState(() => localStorage.getItem('hasArm') === 'true'); + const [lightPos, setLightPos] = useState(() => { + const saved = localStorage.getItem('lightPos'); + return saved ? JSON.parse(saved) : { x: 10, y: 20, z: 10 }; + }); + const [speed, setSpeed] = useState(() => Number(localStorage.getItem('speed')) || 0.1); + const [turnSpeed, setTurnSpeed] = useState(() => Number(localStorage.getItem('turnSpeed')) || 0.05); + const [logs, setLogs] = useState<{ message: string, type: string, time: string }[]>([ + { message: 'System initialized. Waiting for commands...', type: 'info', time: new Date().toLocaleTimeString() } + ]); + const [actionChunks, setActionChunks] = useState([]); + const [activeKeys, setActiveKeys] = useState>({}); + + // Obstacle placement + const [obstacles, setObstacles] = useState(() => { + const saved = localStorage.getItem('customObstacles'); + return saved ? JSON.parse(saved) : []; + }); + const [placementMode, setPlacementMode] = useState<'none' | 'box' | 'cylinder'>('none'); + const [selectedObstacleId, setSelectedObstacleId] = useState(null); + const obstacleMeshes = useRef>(new Map()); + const placementModeRef = useRef(placementMode); + placementModeRef.current = placementMode; + + // Scene presets + const [scenePresets, setScenePresets] = useState(() => { + const saved = localStorage.getItem(SCENE_PRESETS_KEY); + return saved ? JSON.parse(saved) : []; + }); + const [savingPreset, setSavingPreset] = useState(false); + + useEffect(() => { + localStorage.setItem(SCENE_PRESETS_KEY, JSON.stringify(scenePresets)); + }, [scenePresets]); + + // Persist settings + useEffect(() => { + localStorage.setItem('sceneType', sceneType); + localStorage.setItem('sceneSize', sceneSize); + localStorage.setItem('sceneComplexity', sceneComplexity); + localStorage.setItem('hasArm', hasArm.toString()); + localStorage.setItem('lightPos', JSON.stringify(lightPos)); + localStorage.setItem('speed', speed.toString()); + localStorage.setItem('turnSpeed', turnSpeed.toString()); + }, [sceneType, sceneSize, sceneComplexity, hasArm, lightPos, speed, turnSpeed]); + + useEffect(() => { + sim.current.speed = speed; + sim.current.turnSpeed = turnSpeed; + }, [speed, turnSpeed]); + + useEffect(() => { + localStorage.setItem('customObstacles', JSON.stringify(obstacles)); + }, [obstacles]); + + // Cloud Training State + const [trainingMode, setTrainingMode] = useState<'frontend' | 'cloud'>('frontend'); + const [cloudModels, setCloudModels] = useState([]); + const [cloudDatasets, setCloudDatasets] = useState([]); + const [selectedCloudModel, setSelectedCloudModel] = useState(''); + const [selectedCloudDataset, setSelectedCloudDataset] = useState(''); + const [cloudTrainingStatus, setCloudTrainingStatus] = useState(null); + + // Refs for DOM elements updated frequently + const posXRef = useRef(null); + const posZRef = useRef(null); + const rotYRef = useRef(null); + const velocityRef = useRef(null); + const canvasContainerRef = useRef(null); + const cameraCanvasRef = useRef(null); + const logContainerRef = useRef(null); + const smallCanvasRef = useRef(null); + + // Simulation State + const sim = useRef({ + robotState: { x: 0, z: 0, rotation: Math.PI, velocity: 0, angularVelocity: 0 }, + isRecording: false, + isTraining: false, + isInferencing: false, + episodes: [] as any[], + currentEpisode: [] as any[], + target: null as THREE.Mesh | null, + walls: [] as THREE.Mesh[], + environmentGroup: null as THREE.Group | null, + dirLight: null as THREE.DirectionalLight | null, + plane: null as THREE.Mesh | null, + robot: null as THREE.Group | null, + armGroup: null as THREE.Group | null, + arm: null as any, + wheels: [] as THREE.Group[], + camera: null as THREE.PerspectiveCamera | null, + scene: null as THREE.Scene | null, + renderer: null as THREE.WebGLRenderer | null, + onboardCamera: null as THREE.PerspectiveCamera | null, + onboardRenderTarget: null as THREE.WebGLRenderTarget | null, + keys: {} as Record, + speed: 0.1, + turnSpeed: 0.05, + animationFrameId: 0, + inferenceTimeoutId: 0 as unknown as ReturnType, + model: null as tf.LayersModel | null, + recordingIntervalId: null as unknown as ReturnType, + lastX: 0, + lastZ: 0, + stuckCounter: 0, + actionBuffer: [] as any[], + lastInferenceLogTime: 0, + lastManualLogTime: 0 + }); + + const isAtBottom = useRef(true); + const isDragging = useRef(false); + const raycaster = useRef(new THREE.Raycaster()); + const mouse = useRef(new THREE.Vector2()); + + const handleLogScroll = () => { + if (logContainerRef.current) { + const { scrollTop, scrollHeight, clientHeight } = logContainerRef.current; + isAtBottom.current = Math.abs(scrollHeight - scrollTop - clientHeight) < 10; + } + }; + + useEffect(() => { + if (isAtBottom.current && logContainerRef.current) { + logContainerRef.current.scrollTop = logContainerRef.current.scrollHeight; + } + }, [logs]); + + const addLog = useCallback((message: string, type = 'info') => { + setLogs(prev => { + const newLogs = [...prev, { message, type, time: new Date().toLocaleTimeString() }]; + if (newLogs.length > 200) { + return newLogs.slice(-200); + } + return newLogs; + }); + }, []); + + const clearLogs = () => setLogs([]); + + // Obstacle management helpers + const addObstacleToScene = useCallback((obs: Obstacle) => { + const group = sim.current.environmentGroup; + if (!group) return; + let mesh: THREE.Mesh; + if (obs.type === 'cylinder') { + const geo = new THREE.CylinderGeometry(obs.w / 2, obs.w / 2, obs.h, 16); + const mat = new THREE.MeshStandardMaterial({ color: obs.color, roughness: 0.7 }); + mesh = new THREE.Mesh(geo, mat); + } else { + const geo = new THREE.BoxGeometry(obs.w, obs.h, obs.d); + const mat = new THREE.MeshStandardMaterial({ color: obs.color, roughness: 0.7 }); + mesh = new THREE.Mesh(geo, mat); + } + mesh.position.set(obs.x, obs.h / 2, obs.z); + mesh.rotation.y = obs.rotation; + mesh.castShadow = true; + mesh.receiveShadow = true; + mesh.userData = { w: obs.w, d: obs.d, obstacleId: obs.id }; + group.add(mesh); + sim.current.walls.push(mesh); + obstacleMeshes.current.set(obs.id, mesh); + }, []); + + const removeObstacleFromScene = useCallback((id: string) => { + const mesh = obstacleMeshes.current.get(id); + if (!mesh) return; + const group = sim.current.environmentGroup; + if (group) group.remove(mesh); + sim.current.walls = sim.current.walls.filter(w => w.userData.obstacleId !== id); + obstacleMeshes.current.delete(id); + mesh.geometry.dispose(); + (mesh.material as THREE.Material).dispose(); + }, []); + + const updateObstacleInScene = useCallback((id: string, updates: Partial) => { + const mesh = obstacleMeshes.current.get(id); + if (!mesh) return; + if (updates.x !== undefined) mesh.position.x = updates.x; + if (updates.z !== undefined) mesh.position.z = updates.z; + if (updates.h !== undefined) mesh.position.y = updates.h / 2; + if (updates.rotation !== undefined) mesh.rotation.y = updates.rotation; + }, []); + + const addObstacle = useCallback((x: number, z: number, type: 'box' | 'cylinder' = 'box') => { + const w = type === 'cylinder' ? 0.8 + Math.random() * 0.6 : 0.8 + Math.random() * 1.5; + const h = type === 'cylinder' ? 0.6 + Math.random() * 1.0 : 0.8 + Math.random() * 1.5; + const d = type === 'cylinder' ? w : 0.8 + Math.random() * 1.5; + const colors = [0x64748b, 0x475569, 0x78716c, 0x92400e, 0x1e3a5f, 0x7c2d12]; + const color = colors[Math.floor(Math.random() * colors.length)]; + const obs: Obstacle = { id: nextObstacleId(), x, z, w, h, d, color, type, rotation: 0 }; + setObstacles(prev => [...prev, obs]); + addObstacleToScene(obs); + addLog(`Placed ${type} obstacle at (${x.toFixed(1)}, ${z.toFixed(1)})`, 'info'); + }, [addObstacleToScene, addLog]); + + const removeObstacle = useCallback((id: string) => { + removeObstacleFromScene(id); + setObstacles(prev => prev.filter(o => o.id !== id)); + if (selectedObstacleId === id) setSelectedObstacleId(null); + }, [removeObstacleFromScene, selectedObstacleId]); + + const clearAllObstacles = useCallback(() => { + obstacles.forEach(o => removeObstacleFromScene(o.id)); + setObstacles([]); + setSelectedObstacleId(null); + addLog('All custom obstacles cleared', 'info'); + }, [obstacles, removeObstacleFromScene, addLog]); + + // Scene preset management + const saveScenePreset = useCallback((name: string) => { + const target = sim.current.target; + const preset: ScenePreset = { + id: `preset_${Date.now()}`, + name, + sceneType, + sceneSize, + sceneComplexity, + obstacles: obstacles.map(({ id: _, ...rest }) => rest), + targetPos: target ? { x: target.position.x, z: target.position.z } : { x: 0, z: -5 }, + createdAt: new Date().toLocaleString(), + }; + setScenePresets(prev => [...prev, preset]); + setSavingPreset(false); + addLog(`Scene saved: "${name}"`, 'success'); + }, [sceneType, sceneSize, sceneComplexity, obstacles, addLog]); + + const loadScenePreset = useCallback((preset: ScenePreset) => { + // Clear current obstacles from scene + obstacles.forEach(o => removeObstacleFromScene(o.id)); + + // Restore scene settings + setSceneType(preset.sceneType); + setSceneSize(preset.sceneSize); + setSceneComplexity(preset.sceneComplexity); + + // Restore obstacles (re-assign IDs) + const newObstacles: Obstacle[] = preset.obstacles.map(o => ({ + ...o, + id: nextObstacleId(), + })); + setObstacles(newObstacles); + + // Restore target position + if (sim.current.target) { + sim.current.target.position.set(preset.targetPos.x, 0.25, preset.targetPos.z); + } + + addLog(`Scene loaded: "${preset.name}" (${newObstacles.length} obstacles)`, 'success'); + }, [obstacles, removeObstacleFromScene, addLog]); + + const deleteScenePreset = useCallback((id: string) => { + setScenePresets(prev => { + const preset = prev.find(p => p.id === id); + if (preset) addLog(`Scene deleted: "${preset.name}"`, 'info'); + return prev.filter(p => p.id !== id); + }); + }, [addLog]); + + const exportScenePreset = useCallback((preset: ScenePreset) => { + const json = JSON.stringify(preset, null, 2); + const blob = new Blob([json], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `scene_${preset.name.replace(/\s+/g, '_')}.json`; + a.click(); + URL.revokeObjectURL(url); + }, []); + + const importScenePreset = useCallback(() => { + const input = document.createElement('input'); + input.type = 'file'; + input.accept = '.json'; + input.onchange = () => { + const file = input.files?.[0]; + if (!file) return; + const reader = new FileReader(); + reader.onload = () => { + try { + const preset = JSON.parse(reader.result as string) as ScenePreset; + if (!preset.name || !preset.sceneType || !preset.obstacles) { + addLog('Invalid scene file format', 'error'); + return; + } + setScenePresets(prev => [...prev, { ...preset, id: `preset_${Date.now()}` }]); + addLog(`Scene imported: "${preset.name}"`, 'success'); + } catch { + addLog('Failed to parse scene file', 'error'); + } + }; + reader.readAsText(file); + }; + input.click(); + }, [addLog]); + + useEffect(() => { + if (!canvasContainerRef.current) return; + + const container = canvasContainerRef.current; + + // Scene + const scene = createScene(); + sim.current.scene = scene; + + // Camera + const camera = createCamera(container.clientWidth, container.clientHeight); + sim.current.camera = camera; + + // Renderer + const renderer = createRenderer(container.clientWidth, container.clientHeight); + container.appendChild(renderer.domElement); + sim.current.renderer = renderer; + + // Lights + const dirLight = createLights(scene, { x: 10, y: 20, z: 10 }); + sim.current.dirLight = dirLight; + + // Floor + const plane = createFloor(scene); + sim.current.plane = plane; + + // Environment Group + const environmentGroup = new THREE.Group(); + scene.add(environmentGroup); + sim.current.environmentGroup = environmentGroup; + + // Robot + const { robot, onboardCamera, onboardRenderTarget, wheelMeshes, armGroup, lowerArm, elbow, wrist, gripper } = createRobot(scene); + sim.current.robot = robot; + sim.current.armGroup = armGroup; + sim.current.arm = { + lowerArm, elbow, wrist, gripper, + state: 'idle', + grabbedObject: null, + targetRotations: { lowerArm: Math.PI/4, elbow: Math.PI/2.5, wrist: -Math.PI/6 } + }; + sim.current.wheels = wheelMeshes; + sim.current.onboardCamera = onboardCamera; + sim.current.onboardRenderTarget = onboardRenderTarget; + + robot.rotation.y = Math.PI; + + scene.add(robot); + + // Dragging Logic + let draggingTarget = 'none' as 'none' | 'target' | 'obstacle'; + let draggingObstacleId: string | null = null; + + const onMouseDown = (event: MouseEvent) => { + if (!container || !sim.current.camera || !sim.current.target) return; + + const rect = container.getBoundingClientRect(); + mouse.current.x = ((event.clientX - rect.left) / container.clientWidth) * 2 - 1; + mouse.current.y = -((event.clientY - rect.top) / container.clientHeight) * 2 + 1; + + raycaster.current.setFromCamera(mouse.current, sim.current.camera); + + // Placement mode: click on ground to place obstacle + if (placementModeRef.current !== 'none') { + const planeIntersects = raycaster.current.intersectObject(sim.current.plane!); + if (planeIntersects.length > 0) { + const point = planeIntersects[0].point; + addObstacle(point.x, point.z, placementModeRef.current); + } + return; + } + + // Check obstacles first (they are on top visually) + const obstacleMeshArr = Array.from(obstacleMeshes.current.values()); + if (obstacleMeshArr.length > 0) { + const obsIntersects = raycaster.current.intersectObjects(obstacleMeshArr); + if (obsIntersects.length > 0) { + draggingTarget = 'obstacle'; + draggingObstacleId = obsIntersects[0].object.userData.obstacleId; + isDragging.current = true; + setSelectedObstacleId(draggingObstacleId); + if (renderer.domElement) renderer.domElement.style.cursor = 'grabbing'; + return; + } + } + + // Check target ball + const intersects = raycaster.current.intersectObject(sim.current.target); + if (intersects.length > 0) { + draggingTarget = 'target'; + isDragging.current = true; + if (renderer.domElement) renderer.domElement.style.cursor = 'grabbing'; + } + }; + + const onMouseMove = (event: MouseEvent) => { + if (!container || !sim.current.camera || !sim.current.target) return; + + const rect = container.getBoundingClientRect(); + mouse.current.x = ((event.clientX - rect.left) / container.clientWidth) * 2 - 1; + mouse.current.y = -((event.clientY - rect.top) / container.clientHeight) * 2 + 1; + + raycaster.current.setFromCamera(mouse.current, sim.current.camera); + + if (!isDragging.current) { + // Hover cursor + const obstacleMeshArr = Array.from(obstacleMeshes.current.values()); + const obsIntersects = obstacleMeshArr.length > 0 ? raycaster.current.intersectObjects(obstacleMeshArr) : []; + const targetIntersects = raycaster.current.intersectObject(sim.current.target); + if (renderer.domElement) { + if (obsIntersects.length > 0 || targetIntersects.length > 0) { + renderer.domElement.style.cursor = placementModeRef.current !== 'none' ? 'crosshair' : 'grab'; + } else { + renderer.domElement.style.cursor = placementModeRef.current !== 'none' ? 'crosshair' : 'default'; + } + } + return; + } + + if (!sim.current.plane) return; + + const intersects = raycaster.current.intersectObject(sim.current.plane); + + if (intersects.length > 0) { + const point = intersects[0].point; + if (draggingTarget === 'target') { + sim.current.target.position.x = point.x; + sim.current.target.position.z = point.z; + } else if (draggingTarget === 'obstacle' && draggingObstacleId) { + setObstacles(prev => prev.map(o => o.id === draggingObstacleId ? { ...o, x: point.x, z: point.z } : o)); + updateObstacleInScene(draggingObstacleId, { x: point.x, z: point.z }); + } + } + }; + + const onMouseUp = () => { + isDragging.current = false; + draggingTarget = 'none'; + draggingObstacleId = null; + if (renderer.domElement) renderer.domElement.style.cursor = placementModeRef.current !== 'none' ? 'crosshair' : 'default'; + }; + + const onTouchStart = (event: TouchEvent) => { + if (event.touches.length > 0) { + const touch = event.touches[0]; + onMouseDown({ clientX: touch.clientX, clientY: touch.clientY } as MouseEvent); + } + }; + + const onTouchMove = (event: TouchEvent) => { + if (event.touches.length > 0) { + const touch = event.touches[0]; + onMouseMove({ clientX: touch.clientX, clientY: touch.clientY } as MouseEvent); + } + }; + + const onTouchEnd = () => { + onMouseUp(); + }; + + renderer.domElement.addEventListener('mousedown', onMouseDown); + window.addEventListener('mousemove', onMouseMove); + window.addEventListener('mouseup', onMouseUp); + renderer.domElement.addEventListener('touchstart', onTouchStart, { passive: false }); + window.addEventListener('touchmove', onTouchMove, { passive: false }); + window.addEventListener('touchend', onTouchEnd); + + // Resize handler + const onWindowResize = () => { + if (!container || !sim.current.camera || !sim.current.renderer) return; + sim.current.camera.aspect = container.clientWidth / container.clientHeight; + sim.current.camera.updateProjectionMatrix(); + sim.current.renderer.setSize(container.clientWidth, container.clientHeight); + }; + window.addEventListener('resize', onWindowResize); + + return () => { + window.removeEventListener('resize', onWindowResize); + cancelAnimationFrame(sim.current.animationFrameId); + if (container && sim.current.renderer) { + container.removeChild(sim.current.renderer.domElement); + } + }; + }, [addLog]); + + useEffect(() => { + if (sim.current.armGroup) { + sim.current.armGroup.visible = hasArm; + } + }, [hasArm]); + + useEffect(() => { + if (sim.current.dirLight) { + sim.current.dirLight.position.set(lightPos.x, lightPos.y, lightPos.z); + } + }, [lightPos]); + + useEffect(() => { + if (!sim.current.environmentGroup || !sim.current.plane) return; + + const group = sim.current.environmentGroup; + const plane = sim.current.plane; + + while (group.children.length > 0) { + group.remove(group.children[0]); + } + sim.current.walls = []; + + let sizeVal = 20; + if (sceneSize === 'small') sizeVal = 10; + if (sceneSize === 'large') sizeVal = 30; + + const halfSize = sizeVal / 2; + + const createTexture = (type: string) => { + const canvas = document.createElement('canvas'); + canvas.width = 512; + canvas.height = 512; + const ctx = canvas.getContext('2d')!; + + if (type === 'wood') { + ctx.fillStyle = '#8b5a2b'; + ctx.fillRect(0, 0, 512, 512); + for (let i = 0; i < 200; i++) { + ctx.fillStyle = `rgba(60, 30, 10, ${Math.random() * 0.15})`; + ctx.fillRect(0, Math.random() * 512, 512, Math.random() * 10); + } + } else if (type === 'tile') { + ctx.fillStyle = '#e2e8f0'; + ctx.fillRect(0, 0, 512, 512); + ctx.strokeStyle = '#94a3b8'; + ctx.lineWidth = 4; + for (let i = 0; i <= 512; i += 64) { + ctx.beginPath(); ctx.moveTo(i, 0); ctx.lineTo(i, 512); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(0, i); ctx.lineTo(512, i); ctx.stroke(); + } + } else if (type === 'tennis') { + ctx.fillStyle = '#1e5631'; + ctx.fillRect(0, 0, 512, 512); + ctx.strokeStyle = 'white'; + ctx.lineWidth = 4; + ctx.strokeRect(64, 32, 384, 448); + ctx.beginPath(); ctx.moveTo(100, 32); ctx.lineTo(100, 480); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(412, 32); ctx.lineTo(412, 480); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(100, 128); ctx.lineTo(412, 128); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(100, 384); ctx.lineTo(412, 384); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(256, 128); ctx.lineTo(256, 384); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(256, 32); ctx.lineTo(256, 40); ctx.stroke(); + ctx.beginPath(); ctx.moveTo(256, 480); ctx.lineTo(256, 472); ctx.stroke(); + ctx.lineWidth = 6; + ctx.beginPath(); ctx.moveTo(64, 256); ctx.lineTo(448, 256); ctx.stroke(); + } else if (type === 'wall') { + ctx.fillStyle = '#f8fafc'; + ctx.fillRect(0, 0, 512, 512); + for (let i = 0; i < 500; i++) { + ctx.fillStyle = `rgba(0,0,0,${Math.random() * 0.03})`; + ctx.beginPath(); + ctx.arc(Math.random() * 512, Math.random() * 512, Math.random() * 2, 0, Math.PI * 2); + ctx.fill(); + } + } + + const tex = new THREE.CanvasTexture(canvas); + tex.wrapS = THREE.RepeatWrapping; + tex.wrapT = THREE.RepeatWrapping; + if (type !== 'tennis') tex.repeat.set(sizeVal / 5, sizeVal / 5); + return tex; + }; + + const woodTex = createTexture('wood'); + const tileTex = createTexture('tile'); + const tennisTex = createTexture('tennis'); + const wallTex = createTexture('wall'); + + const planeMat = plane.material as THREE.MeshStandardMaterial; + if (sceneType === 'living_room') { + planeMat.map = woodTex; + planeMat.color.setHex(0xffffff); + } else if (sceneType === 'classroom') { + planeMat.map = tileTex; + planeMat.color.setHex(0xffffff); + } else if (sceneType === 'tennis_court') { + planeMat.map = tennisTex; + planeMat.color.setHex(0xffffff); + } else { + planeMat.map = null; + planeMat.color.setHex(0x1e293b); + } + planeMat.needsUpdate = true; + + const wallMat = new THREE.MeshStandardMaterial({ map: wallTex, color: 0xd1d5db, roughness: 0.9 }); + + const addWall = (x: number, z: number, w: number, h: number, d: number, color?: number, map?: THREE.Texture) => { + const mat = color ? new THREE.MeshStandardMaterial({ color, map: map || null }) : wallMat; + const geo = new THREE.BoxGeometry(w, h, d); + const mesh = new THREE.Mesh(geo, mat); + mesh.position.set(x, h / 2, z); + mesh.castShadow = true; + mesh.receiveShadow = true; + mesh.userData = { w, d }; + group.add(mesh); + sim.current.walls.push(mesh); + }; + + if (sceneType !== 'tennis_court') { + addWall(0, -halfSize, sizeVal, 2, 0.5); + addWall(0, halfSize, sizeVal, 2, 0.5); + addWall(-halfSize, 0, 0.5, 2, sizeVal); + addWall(halfSize, 0, 0.5, 2, sizeVal); + } else { + addWall(0, -30, 60, 2, 0.5); + addWall(0, 30, 60, 2, 0.5); + addWall(-30, 0, 0.5, 2, 60); + addWall(30, 0, 0.5, 2, 60); + } + + let numObstacles = 0; + if (sceneComplexity === 'medium') numObstacles = 5; + if (sceneComplexity === 'high') numObstacles = 12; + + if (sceneType === 'basic') { + for (let i = 0; i < numObstacles; i++) { + const w = 1 + Math.random() * 2; + const d = 1 + Math.random() * 2; + const x = (Math.random() - 0.5) * (sizeVal - 4); + const z = (Math.random() - 0.5) * (sizeVal - 4); + if (Math.abs(x) < 2 && Math.abs(z) < 2) continue; + addWall(x, z, w, 1.5, d, 0x64748b); + } + } else if (sceneType === 'living_room') { + addWall(0, -halfSize + 2, 4, 1, 1.5, 0x334155); + addWall(0, -halfSize + 1.2, 4, 2, 0.5, 0x334155); + addWall(0, halfSize - 1, 3, 0.8, 1, 0x8b5cf6); + addWall(0, -halfSize + 4, 2, 0.5, 1.5, 0xffffff, woodTex); + + for (let i = 0; i < numObstacles - 3; i++) { + const w = 0.8 + Math.random() * 1; + const d = 0.8 + Math.random() * 1; + const x = (Math.random() - 0.5) * (sizeVal - 4); + const z = (Math.random() - 0.5) * (sizeVal - 4); + if (Math.abs(x) < 3 && Math.abs(z) < 3) continue; + addWall(x, z, w, 1 + Math.random(), d, 0x475569); + } + } else if (sceneType === 'classroom') { + const rows = Math.min(4, Math.max(2, Math.floor(numObstacles / 2))); + const cols = Math.min(4, Math.max(2, Math.floor(numObstacles / 2))); + for (let r = 0; r < rows; r++) { + for (let c = 0; c < cols; c++) { + const x = -halfSize / 2 + 2 + c * 3; + const z = -halfSize / 2 + 2 + r * 3; + if (Math.abs(x) < 2 && Math.abs(z) < 2) continue; + addWall(x, z, 1.5, 0.8, 1, 0xffffff, woodTex); + } + } + addWall(0, halfSize - 2, 3, 1, 1.5, 0xffffff, woodTex); + } else if (sceneType === 'tennis_court') { + const netMat = new THREE.MeshStandardMaterial({ color: 0xffffff, transparent: true, opacity: 0.5, wireframe: true }); + const netGeo = new THREE.BoxGeometry(60, 1, 0.5); + const net = new THREE.Mesh(netGeo, netMat); + net.position.set(0, 0.5, 0); + group.add(net); + } + + const ballGeo = new THREE.SphereGeometry(0.25, 16, 16); + const ballMat = new THREE.MeshStandardMaterial({ color: 0xccff00, roughness: 0.8 }); + const ball = new THREE.Mesh(ballGeo, ballMat); + ball.position.set(0, 0.25, -5); + ball.castShadow = true; + ball.userData = { w: 0.5, d: 0.5 }; + group.add(ball); + sim.current.walls.push(ball); + sim.current.target = ball; // Use ball as the target since red cube is removed + + addLog(`Scene updated: ${sceneType}, Size: ${sceneSize}, Complexity: ${sceneComplexity}`, 'info'); + + // Restore custom obstacles after scene regeneration + obstacles.forEach(obs => addObstacleToScene(obs)); + }, [sceneType, sceneSize, sceneComplexity, addLog, obstacles, addObstacleToScene]); + + const [enableCollisionProtection, setEnableCollisionProtection] = useState(() => { + const saved = localStorage.getItem('enableCollisionProtection'); + return saved !== null ? saved === 'true' : true; + }); + + useEffect(() => { + localStorage.setItem('enableCollisionProtection', enableCollisionProtection.toString()); + }, [enableCollisionProtection]); + + const captureImage = useCallback(() => { + if (!cameraCanvasRef.current) return null; + + if (!smallCanvasRef.current) { + const c = document.createElement('canvas'); + c.width = 64; + c.height = 64; + smallCanvasRef.current = c; + } + + const smallCtx = smallCanvasRef.current.getContext('2d', { willReadFrequently: true }); + if (smallCtx) { + smallCtx.drawImage(cameraCanvasRef.current, 0, 0, 64, 64); + } + + return tf.tidy(() => { + const img = tf.browser.fromPixels(smallCanvasRef.current!); + const normalized = img.div(255.0); + return normalized.arraySync() as number[][][]; + }); + }, []); + + const recordFrame = useCallback(() => { + if (!sim.current.target || !sim.current.isRecording) return; + + // Smart Recording: Check for collision + if (enableCollisionProtection && sim.current.isColliding) { + addLog('Collision detected! Stopping recording and discarding last 1s...', 'warning'); + + // Discard last 1 second (approx 10 frames at 10Hz recording) + const framesToDiscard = 10; + if (sim.current.currentEpisode.length > framesToDiscard) { + sim.current.currentEpisode.splice(-framesToDiscard, framesToDiscard); + } else { + sim.current.currentEpisode = []; + } + + toggleRecording(); // Stop recording + return; + } + + const image = captureImage(); + if (!image) return; + + // Use small canvas for Base64 - much faster + const imageBase64 = smallCanvasRef.current?.toDataURL('image/jpeg', 0.7).split(',')[1]; + + // Match reference state structure: + // state: [x, y, angle, speed, ballDist, isColliding, ...zeros] (14 dims) + + const x = sim.current.robotState.x; + const z = sim.current.robotState.z; // treating z as y in 2D + const angle = sim.current.robotState.rotation; + const speed = sim.current.robotState.velocity; + + const targetDist = Math.hypot(sim.current.target.position.x - x, sim.current.target.position.z - z); + + const state = new Array(14).fill(0); + state[0] = x; + state[1] = z; + state[2] = angle; + state[3] = speed; + state[4] = targetDist; + state[5] = sim.current.isColliding ? 1.0 : 0.0; // Explicitly tell model we are colliding + + const envState = new Array(10).fill(0); + envState[0] = x; + envState[1] = z; + envState[2] = angle; + envState[3] = speed; + envState[4] = sim.current.isColliding ? 1 : 0; + envState[5] = targetDist; + // Relative angle to ball (normalized to [-PI, PI]) + const relAngleToBall = Math.atan2( + sim.current.target.position.x - x, + sim.current.target.position.z - z + ) - angle; + envState[6] = Math.sin(relAngleToBall); // sin component for angle diff + envState[7] = Math.cos(relAngleToBall); // cos component for angle diff + // Ball position relative to robot + envState[8] = sim.current.target.position.x - x; + envState[9] = sim.current.target.position.z - z; + + // Action: [velocity, angularVelocity] (continuous, 2-dim) + // Supports simultaneous forward + turning, matching real robot control. + const keys = sim.current.keys; + let v = 0, w = 0; + if (keys['w'] || keys['arrowup']) v += sim.current.speed; + if (keys['s'] || keys['arrowdown']) v -= sim.current.speed; + if (keys['a'] || keys['arrowleft']) w += sim.current.turnSpeed; + if (keys['d'] || keys['arrowright']) w -= sim.current.turnSpeed; + const action = [v, w]; + + const frame = { + state: state, + envState: envState, + image: image, + imageBase64: imageBase64, + action: action + }; + + sim.current.currentEpisode.push(frame); + + // Throttle UI updates to avoid lag + if (sim.current.currentEpisode.length % 5 === 0) { + setFrameCount(sim.current.currentEpisode.length); + setActionCount(sim.current.currentEpisode.length); + } + }, [captureImage]); + + const sendCommand = useCallback((cmd: string) => { + if (sim.current.isTraining) return; + + switch (cmd) { + case 'forward': sim.current.robotState.velocity = speed; break; + case 'backward': sim.current.robotState.velocity = -speed; break; + case 'left': sim.current.robotState.angularVelocity = turnSpeed; break; + case 'right': sim.current.robotState.angularVelocity = -turnSpeed; break; + } + + addLog(`Command: ${cmd.toUpperCase()}`, 'info'); + }, [addLog, speed, turnSpeed]); + + useEffect(() => { + const handleKeyDown = (e: KeyboardEvent) => { + const key = e.key.toLowerCase(); + sim.current.keys[key] = true; + if (['w', 'a', 's', 'd', 'q', 'e', 'arrowup', 'arrowdown', 'arrowleft', 'arrowright'].includes(key)) { + e.preventDefault(); + } + // Obstacle interaction shortcuts (only when not in placement mode) + if (placementMode === 'none' && selectedObstacleId) { + if (key === 'r') { + setObstacles(prev => prev.map(o => { + if (o.id !== selectedObstacleId) return o; + const newRot = o.rotation + Math.PI / 12; + updateObstacleInScene(o.id, { rotation: newRot }); + return { ...o, rotation: newRot }; + })); + } + if (key === 'delete' || key === 'backspace') { + e.preventDefault(); + removeObstacle(selectedObstacleId); + } + } + }; + + const handleKeyUp = (e: KeyboardEvent) => { + sim.current.keys[e.key.toLowerCase()] = false; + }; + + document.addEventListener('keydown', handleKeyDown); + document.addEventListener('keyup', handleKeyUp); + return () => { + document.removeEventListener('keydown', handleKeyDown); + document.removeEventListener('keyup', handleKeyUp); + }; + }, [placementMode, selectedObstacleId, removeObstacle, updateObstacleInScene]); + + const updateRobotMovement = useCallback(() => { + if (sim.current.isInferencing || sim.current.isTraining) return; + + const keys = sim.current.keys; + let v = 0; + let w = 0; + + if (keys['w'] || keys['arrowup']) v += speed; + if (keys['s'] || keys['arrowdown']) v -= speed; + if (keys['a'] || keys['arrowleft']) w += turnSpeed; + if (keys['d'] || keys['arrowright']) w -= turnSpeed; + + if (v !== 0 || w !== 0) { + if (!sim.current.lastManualLogTime || Date.now() - sim.current.lastManualLogTime > 200) { + addLog(`Manual Movement: V=${v.toFixed(2)}, W=${w.toFixed(2)}`, 'info'); + sim.current.lastManualLogTime = Date.now(); + } + } + + // Update active keys for UI highlighting + const newActiveKeys: Record = {}; + if (v > 0) newActiveKeys['w'] = true; + if (v < 0) newActiveKeys['s'] = true; + if (w > 0) newActiveKeys['a'] = true; + if (w < 0) newActiveKeys['d'] = true; + if (keys['q']) newActiveKeys['q'] = true; + if (keys['e']) newActiveKeys['e'] = true; + + setActiveKeys(prev => { + const keysChanged = Object.keys(newActiveKeys).length !== Object.keys(prev).length || + Object.keys(newActiveKeys).some(k => newActiveKeys[k] !== prev[k]); + return keysChanged ? newActiveKeys : prev; + }); + + sim.current.robotState.velocity = v; + sim.current.robotState.angularVelocity = w; + }, [speed, turnSpeed, addLog]); + + const updatePhysics = useCallback(() => { + const state = sim.current.robotState; + const robot = sim.current.robot; + const camera = sim.current.camera; + + const nextX = state.x + Math.sin(state.rotation) * state.velocity; + const nextZ = state.z + Math.cos(state.rotation) * state.velocity; + + let collided = false; + const robotRadius = 0.9; // Increased to cover camera offset (1.2) and body + const checkAABB = (objX: number, objZ: number, w: number, d: number) => { + return (nextX > objX - w / 2 - robotRadius && + nextX < objX + w / 2 + robotRadius && + nextZ > objZ - d / 2 - robotRadius && + nextZ < objZ + d / 2 + robotRadius); + }; + + sim.current.walls.forEach(wall => { + if (checkAABB(wall.position.x, wall.position.z, wall.userData.w, wall.userData.d)) { + collided = true; + } + }); + + if (sim.current.target && checkAABB(sim.current.target.position.x, sim.current.target.position.z, sim.current.target.userData.w, sim.current.target.userData.d)) { + collided = true; + } + + sim.current.isColliding = collided; // Expose collision state + + if (!collided) { + state.x = nextX; + state.z = nextZ; + } else { + state.velocity = 0; + } + + state.rotation += state.angularVelocity; + + // Apply friction decay only in manual mode (skip during inference) + if (!sim.current.isInferencing) { + state.velocity *= 0.9; + state.angularVelocity *= 0.9; + } + + if (robot) { + robot.position.x = state.x; + robot.position.z = state.z; + robot.rotation.y = state.rotation; + + if (sim.current.wheels && sim.current.wheels.length === 2) { + const wheelRadius = 0.3; + const rotationAmount = state.velocity / wheelRadius; + // Also add differential rotation for turning + const turnRotation = state.angularVelocity * 0.5 / wheelRadius; + + sim.current.wheels[0].rotateY(-rotationAmount - turnRotation); // Right wheel + sim.current.wheels[1].rotateY(-rotationAmount + turnRotation); // Left wheel + } + } + + if (camera) { + camera.position.x = state.x; + camera.position.z = state.z + 12; + camera.lookAt(state.x, 0, state.z); + } + + if (posXRef.current) posXRef.current.textContent = state.x.toFixed(2); + if (posZRef.current) posZRef.current.textContent = state.z.toFixed(2); + if (rotYRef.current) rotYRef.current.textContent = (state.rotation * 180 / Math.PI).toFixed(0) + '°'; + if (velocityRef.current) velocityRef.current.textContent = (Math.abs(state.velocity) * 10).toFixed(1); + }, []); + + const updateOnboardCamera = useCallback(() => { + const { onboardCamera, onboardRenderTarget, renderer, scene, robotState } = sim.current; + const canvas = cameraCanvasRef.current; + if (!onboardCamera || !onboardRenderTarget || !renderer || !scene || !canvas) return; + + const dirX = Math.sin(robotState.rotation); + const dirZ = Math.cos(robotState.rotation); + + onboardCamera.position.set( + robotState.x + dirX * 0.85, + 0.7, + robotState.z + dirZ * 0.85 + ); + + onboardCamera.lookAt( + robotState.x + dirX * 10.0, + 0.7, + robotState.z + dirZ * 10.0 + ); + + renderer.setRenderTarget(onboardRenderTarget); + renderer.clear(); + renderer.render(scene, onboardCamera); + renderer.setRenderTarget(null); + + const ctx = canvas.getContext('2d', { willReadFrequently: true }); + if (!ctx) return; + canvas.width = 320; + canvas.height = 240; + + const pixels = new Uint8Array(320 * 240 * 4); + renderer.readRenderTargetPixels(onboardRenderTarget, 0, 0, 320, 240, pixels); + + const imageData = ctx.createImageData(320, 240); + + for (let y = 0; y < 240; y++) { + for (let x = 0; x < 320; x++) { + const srcIdx = ((239 - y) * 320 + x) * 4; + const dstIdx = (y * 320 + x) * 4; + + imageData.data[dstIdx] = pixels[srcIdx]; + imageData.data[dstIdx + 1] = pixels[srcIdx + 1]; + imageData.data[dstIdx + 2] = pixels[srcIdx + 2]; + imageData.data[dstIdx + 3] = 255; + } + } + + ctx.putImageData(imageData, 0, 0); + }, []); + + const updateArm = useCallback(() => { + if (!sim.current.arm || !hasArm) return; + const arm = sim.current.arm; + + // Handle inputs + if (sim.current.keys['q']) { + sim.current.keys['q'] = false; + if (arm.state === 'idle') { + // Find object + const robotPos = sim.current.robot!.position.clone(); + const forward = new THREE.Vector3(0, 0, 1).applyQuaternion(sim.current.robot!.quaternion); + const grabPos = robotPos.clone().add(forward.multiplyScalar(1.5)); + + let closestObj = null; + let minDistance = 1.5; + + for (const obj of sim.current.walls) { + if (obj.geometry.type === 'SphereGeometry' || obj.userData.isGrabbable) { + const dist = obj.position.distanceTo(grabPos); + if (dist < minDistance) { + minDistance = dist; + closestObj = obj; + } + } + } + + if (closestObj) { + arm.grabbedObject = closestObj; + arm.state = 'picking_down'; + arm.targetRotations = { lowerArm: Math.PI/2.2, elbow: Math.PI/4, wrist: Math.PI/4 }; + addLog('Picking up object...', 'info'); + } else { + addLog('No object in range to pick up', 'warn'); + } + } + } + + if (sim.current.keys['e']) { + sim.current.keys['e'] = false; + if (arm.state === 'holding') { + arm.state = 'dropping_down'; + arm.targetRotations = { lowerArm: Math.PI/2.2, elbow: Math.PI/4, wrist: Math.PI/4 }; + addLog('Dropping object...', 'info'); + } + } + + // Animate joints + arm.lowerArm.rotation.x += (arm.targetRotations.lowerArm - arm.lowerArm.rotation.x) * 0.1; + arm.elbow.rotation.x += (arm.targetRotations.elbow - arm.elbow.rotation.x) * 0.1; + arm.wrist.rotation.x += (arm.targetRotations.wrist - arm.wrist.rotation.x) * 0.1; + + // State machine transitions + const isAtTarget = + Math.abs(arm.lowerArm.rotation.x - arm.targetRotations.lowerArm) < 0.05 && + Math.abs(arm.elbow.rotation.x - arm.targetRotations.elbow) < 0.05 && + Math.abs(arm.wrist.rotation.x - arm.targetRotations.wrist) < 0.05; + + if (isAtTarget) { + if (arm.state === 'picking_down') { + if (arm.grabbedObject) { + arm.gripper.add(arm.grabbedObject); + arm.grabbedObject.position.set(0, 0.35, 0); + } + arm.state = 'picking_up'; + arm.targetRotations = { lowerArm: -Math.PI/6, elbow: Math.PI/1.5, wrist: -Math.PI/4 }; + } else if (arm.state === 'picking_up') { + arm.state = 'holding'; + addLog('Object picked up', 'success'); + } else if (arm.state === 'dropping_down') { + if (arm.grabbedObject) { + const worldPos = new THREE.Vector3(); + arm.grabbedObject.getWorldPosition(worldPos); + sim.current.environmentGroup!.add(arm.grabbedObject); + arm.grabbedObject.position.copy(worldPos); + // Drop to ground level + arm.grabbedObject.position.y = 0.25; + arm.grabbedObject = null; + } + arm.state = 'dropping_up'; + arm.targetRotations = { lowerArm: Math.PI/4, elbow: Math.PI/2.5, wrist: -Math.PI/6 }; + } else if (arm.state === 'dropping_up') { + arm.state = 'idle'; + addLog('Object dropped', 'success'); + } + } + }, [hasArm, addLog]); + + const animate = useCallback(() => { + sim.current.animationFrameId = requestAnimationFrame(animate); + + updateRobotMovement(); + updatePhysics(); + updateOnboardCamera(); + updateArm(); + + if (sim.current.renderer && sim.current.scene && sim.current.camera) { + sim.current.renderer.render(sim.current.scene, sim.current.camera); + } + }, [updateRobotMovement, updatePhysics, updateOnboardCamera, updateArm]); + + useEffect(() => { + sim.current.animationFrameId = requestAnimationFrame(animate); + return () => cancelAnimationFrame(sim.current.animationFrameId); + }, [animate]); + + const toggleRecording = () => { + if (!sim.current.isRecording) { + sim.current.isRecording = true; + sim.current.currentEpisode = []; + setIsRecording(true); + addLog('Started recording episode...', 'success'); + + sim.current.recordingIntervalId = setInterval(recordFrame, 100); + } else { + sim.current.isRecording = false; + setIsRecording(false); + if (sim.current.recordingIntervalId) clearInterval(sim.current.recordingIntervalId); + + if (sim.current.currentEpisode.length > 0) { + sim.current.episodes.push([...sim.current.currentEpisode]); + setEpisodesCount(sim.current.episodes.length); + addLog(`Episode saved. Total episodes: ${sim.current.episodes.length}`, 'success'); + } + } + }; + + const resetRobot = () => { + sim.current.robotState.x = 0; + sim.current.robotState.z = 0; + sim.current.robotState.rotation = Math.PI; + sim.current.robotState.velocity = 0; + sim.current.robotState.angularVelocity = 0; + setActiveKeys({}); + setActionChunks([]); + + if (sim.current.robot) { + sim.current.robot.position.set(0, 0, 0); + sim.current.robot.rotation.y = Math.PI; + } + + if (sim.current.target) { + sim.current.target.position.set(0, 0.25, -5); + } + + addLog('Robot and target reset', 'warning'); + }; + + const saveDataset = () => { + if (trainingMode === 'cloud') { + saveCloudDataset(); + return; + } + + if (sim.current.episodes.length === 0) return; + + const dataset = { + metadata: { + robot_type: "diff_drive", + action_space: "discrete_5", + fps: 10, + created: new Date().toISOString() + }, + episodes: sim.current.episodes + }; + + const blob = new Blob([JSON.stringify(dataset, null, 2)], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `lerobot_act_dataset_${Date.now()}.json`; + a.click(); + + addLog('Dataset downloaded successfully', 'success'); + }; + + const clearDataset = () => { + sim.current.episodes = []; + setEpisodesCount(0); + setFrameCount(0); + setActionCount(0); + addLog('Dataset cleared.', 'info'); + }; + + const handleImportDataset = (event: React.ChangeEvent) => { + const file = event.target.files?.[0]; + if (!file) return; + + const reader = new FileReader(); + reader.onload = (e) => { + try { + const content = e.target?.result as string; + const dataset = JSON.parse(content); + + if (dataset.episodes && Array.isArray(dataset.episodes)) { + sim.current.episodes = dataset.episodes; + setEpisodesCount(dataset.episodes.length); + + // Calculate total frames and actions + let totalFrames = 0; + dataset.episodes.forEach((ep: any[]) => totalFrames += ep.length); + setFrameCount(totalFrames); + setActionCount(totalFrames); // Assuming 1 action per frame + + addLog(`Dataset imported: ${dataset.episodes.length} episodes`, 'success'); + } else { + addLog('Invalid dataset format', 'error'); + } + } catch (err) { + console.error(err); + addLog('Failed to parse dataset file', 'error'); + } + }; + reader.readAsText(file); + }; + + const loadTrainedModels = useCallback(async () => { + try { + const models = await tf.io.listModels(); + const loadedModels = Object.keys(models) + .filter(key => key.startsWith('indexeddb://')) + .map(key => ({ name: key.replace('indexeddb://', '') })); + + setTrainedModels([ + { name: '跟随网球示例' }, + { name: '自动避障示例' }, + ...loadedModels + ]); + } catch (err) { + console.error('Failed to load models from IndexedDB', err); + } + }, []); + + const handleExportModels = async () => { + const modelsToExport = trainedModels.filter(m => !m.name.endsWith('示例')); + if (modelsToExport.length === 0) { + addLog('No user-trained models to export.', 'warning'); + return; + } + addLog('Preparing models for export...', 'info'); + try { + const JSZip = (await import('jszip')).default; + const zip = new JSZip(); + + for (const modelInfo of modelsToExport) { + const modelName = modelInfo.name; + const model = await tf.loadLayersModel('indexeddb://' + modelName); + + await model.save(tf.io.withSaveHandler(async (artifacts) => { + const folder = zip.folder(modelName); + if (folder) { + folder.file(`${modelName}.json`, JSON.stringify({ + modelTopology: artifacts.modelTopology, + format: artifacts.format, + generatedBy: artifacts.generatedBy, + convertedBy: artifacts.convertedBy, + weightsManifest: [{ + paths: [`${modelName}.weights.bin`], + weights: artifacts.weightSpecs + }] + })); + if (artifacts.weightData) { + let data: ArrayBuffer; + if (Array.isArray(artifacts.weightData)) { + const totalLength = artifacts.weightData.reduce((acc, val) => acc + val.byteLength, 0); + const tmp = new Uint8Array(totalLength); + let offset = 0; + for (const buf of artifacts.weightData) { + tmp.set(new Uint8Array(buf), offset); + offset += buf.byteLength; + } + data = tmp.buffer; + } else { + data = artifacts.weightData; + } + folder.file(`${modelName}.weights.bin`, data); + } + } + return { modelArtifactsInfo: { dateSaved: new Date(), modelTopologyType: 'JSON' } }; + })); + } + + const blob = await zip.generateAsync({ type: 'blob' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `lerobot_models_${Date.now()}.zip`; + a.click(); + URL.revokeObjectURL(url); + addLog('Models exported successfully.', 'success'); + } catch (err) { + console.error('Export failed:', err); + addLog('Failed to export models.', 'error'); + } + }; + + const handleImportModelsFolder = async (e: React.ChangeEvent) => { + const files = e.target.files; + if (!files || files.length === 0) return; + + addLog('Importing models...', 'info'); + const modelFiles: Record = {}; + + for (let i = 0; i < files.length; i++) { + const file = files[i]; + const parts = file.webkitRelativePath.split('/'); + if (parts.length < 2) continue; + + const fileName = file.name; + const modelName = parts[parts.length - 2]; + + if (!modelFiles[modelName]) modelFiles[modelName] = {}; + + if (fileName.endsWith('.json')) { + modelFiles[modelName].json = file; + } else if (fileName.endsWith('.bin')) { + modelFiles[modelName].bin = file; + } + } + + let importCount = 0; + for (const [modelName, pair] of Object.entries(modelFiles)) { + if (pair.json && pair.bin) { + try { + const model = await tf.loadLayersModel(tf.io.browserFiles([pair.json, pair.bin])); + await model.save('indexeddb://' + modelName); + importCount++; + } catch (err) { + console.error(`Failed to import model ${modelName}:`, err); + addLog(`Failed to import ${modelName}`, 'error'); + } + } + } + + if (importCount > 0) { + addLog(`Successfully imported ${importCount} models.`, 'success'); + loadTrainedModels(); + } else { + addLog('No valid models found in the selected folder.', 'warning'); + } + + e.target.value = ''; + }; + + const finishTraining = useCallback(async () => { + sim.current.isTraining = false; + setIsTraining(false); + const newModelName = `ACT_Model_v${trainedModels.length + 1}`; + + if (sim.current.model) { + try { + await sim.current.model.save(`indexeddb://${newModelName}`); + } catch (err) { + console.error('Failed to save model to IndexedDB', err); + } + } + + const newModel = { name: newModelName }; + setTrainedModels(prev => [...prev, newModel]); + setTrainedModel(newModel); + setSelectedModel(newModelName); + + addLog('Training complete! Model ready for inference.', 'success'); + addLog(`Model: ${newModelName} with CVAE prior, Chunk size: 8`, 'info'); + }, [addLog, trainedModels.length]); + + // Cloud Functions + const fetchCloudModels = useCallback(async () => { + const models = await cloudService.fetchModels(); + setCloudModels(models); + if (models.length > 0 && !selectedCloudModel) { + setSelectedCloudModel(models[0].id); + } + }, [selectedCloudModel]); + + const fetchCloudDatasets = useCallback(async () => { + const datasets = await cloudService.fetchDatasets(); + setCloudDatasets(datasets); + if (datasets.length > 0 && !selectedCloudDataset) { + setSelectedCloudDataset(datasets[0].path); + } + }, [selectedCloudDataset]); + + useEffect(() => { + if (trainingMode === 'cloud') { + fetchCloudModels(); + fetchCloudDatasets(); + } + }, [trainingMode, fetchCloudModels, fetchCloudDatasets]); + + const CHUNK_SIZE = 10; + const ACTION_DIM = 2; + + const packDataset = (episodes: any[]) => { + const states: number[][] = []; + const env_states: number[][] = []; + const actions: number[][][] = []; + const action_is_pad: number[][] = []; + const images: string[][][] = []; + let hasImages = false; + + episodes.forEach(ep => { + for (let i = 0; i < ep.length; i += CHUNK_SIZE) { + const chunkSteps = ep.slice(i, i + CHUNK_SIZE); + const firstStep = chunkSteps[0]; + + const stateChunk = firstStep.state || new Array(14).fill(0); + const envChunk = firstStep.envState || new Array(10).fill(0); + + const actionChunk: number[][] = []; + const padChunk: number[] = []; + const imageChunk: string[][] = []; + + chunkSteps.forEach((step: any) => { + actionChunk.push(step.action); + padChunk.push(0); + const image = step.imageBase64 || ""; + if (image) hasImages = true; + imageChunk.push([image]); + }); + + for (let pad = chunkSteps.length; pad < CHUNK_SIZE; pad += 1) { + actionChunk.push(new Array(ACTION_DIM).fill(0)); + padChunk.push(1); + imageChunk.push([""]); + } + + states.push(stateChunk); + env_states.push(envChunk); + actions.push(actionChunk); + action_is_pad.push(padChunk); + images.push(imageChunk); + } + }); + + if (hasImages) { + return { states, env_states, actions, action_is_pad, images }; + } + return { states, env_states, actions, action_is_pad }; + }; + + const saveCloudDataset = async () => { + if (sim.current.episodes.length === 0) return; + addLog('Uploading dataset to cloud...', 'info'); + + const dataset = packDataset(sim.current.episodes); + const success = await cloudService.saveDataset(dataset); + + if (success) { + addLog('Dataset uploaded successfully!', 'success'); + fetchCloudDatasets(); + } else { + addLog('Failed to upload dataset.', 'error'); + } + }; + + const startCloudTraining = async () => { + if (!selectedCloudDataset) { + addLog('Please select a dataset for cloud training.', 'error'); + return; + } + + setIsTraining(true); + setTrainingStatus('Initiating Cloud Training...'); + + const success = await cloudService.startTraining(selectedCloudDataset); + + if (success) { + addLog('Cloud training started.', 'success'); + // Poll for status + const interval = setInterval(async () => { + const status = await cloudService.getTrainingStatus(); + setCloudTrainingStatus(status); + + if (status) { + if (status.num_epochs > 0) { + setTrainingProgress((status.epoch / status.num_epochs) * 100); + setTrainingStatus(`Cloud Training: Epoch ${status.epoch}/${status.num_epochs} - Loss: ${status.avg_loss?.toFixed(4) ?? 'N/A'}`); + } + + if (status.status === 'completed' || status.status === 'failed') { + clearInterval(interval); + setIsTraining(false); + if (status.status === 'completed') { + addLog('Cloud training completed!', 'success'); + fetchCloudModels(); // Refresh models list + } else { + addLog(`Cloud training failed: ${status.error || 'Unknown error'}`, 'error'); + } + } + } + }, 2000); + } else { + setIsTraining(false); + addLog('Failed to start cloud training.', 'error'); + } + }; + + const runCloudInference = useCallback(async () => { + if (!sim.current.isInferencing) return; + + const x = sim.current.robotState.x; + const z = sim.current.robotState.z; + const angle = sim.current.robotState.rotation; + const speed = sim.current.robotState.velocity; + const targetDist = sim.current.target ? Math.hypot(sim.current.target.position.x - x, sim.current.target.position.z - z) : 0; + + const state = new Array(14).fill(0); + state[0] = x; + state[1] = z; + state[2] = angle; + state[3] = speed; + state[4] = targetDist; + state[5] = sim.current.isColliding ? 1.0 : 0.0; + + const envState = new Array(10).fill(0); + envState[0] = x; + envState[1] = z; + envState[2] = angle; + envState[3] = speed; + envState[4] = sim.current.isColliding ? 1 : 0; + envState[5] = targetDist; + if (sim.current.target) { + const relAngleToBall = Math.atan2( + sim.current.target.position.x - x, + sim.current.target.position.z - z + ) - angle; + envState[6] = Math.sin(relAngleToBall); + envState[7] = Math.cos(relAngleToBall); + envState[8] = sim.current.target.position.x - x; + envState[9] = sim.current.target.position.z - z; + } + + const action = await cloudService.runInferenceStep(state, envState); + + if (!action) { + return; + } + + // Handle continuous action vector [velocity, angularVelocity] + let v = 0; + let w = 0; + if (Array.isArray(action)) { + v = action[0] || 0; + w = action[1] || 0; + } else if (typeof action === 'string') { + // Legacy discrete command fallback + const moveSpeed = 0.1; + const turnSpeedVal = 0.05; + switch (action) { + case 'up': v = moveSpeed; break; + case 'down': v = -moveSpeed; break; + case 'left': w = turnSpeedVal; break; + case 'right': w = -turnSpeedVal; break; + case 'stop': v = 0; w = 0; break; + } + } + + // Clamp + v = Math.max(-0.15, Math.min(0.15, v)); + w = Math.max(-0.08, Math.min(0.08, w)); + + sim.current.robotState.velocity = v; + sim.current.robotState.angularVelocity = w; + + sim.current.inferenceTimeoutId = setTimeout(runCloudInference, 200); + }, [captureImage]); + + const startCloudInference = async () => { + if (!selectedCloudModel) { + addLog('Please select a cloud model.', 'error'); + return; + } + + const success = await cloudService.startInference(selectedCloudModel); + + if (success) { + sim.current.isInferencing = true; + setIsInferencing(true); + addLog('Cloud inference started.', 'success'); + runCloudInference(); + } else { + addLog('Failed to start cloud inference.', 'error'); + } + }; + + const stopInference = useCallback(async () => { + sim.current.isInferencing = false; + setIsInferencing(false); + clearTimeout(sim.current.inferenceTimeoutId); + setActiveKeys({}); + setActionChunks([]); + + if (trainingMode === 'cloud') { + const success = await cloudService.stopInference(); + if (success) { + addLog('Cloud inference stopped.', 'warning'); + } + } else { + addLog('Inference stopped.', 'warning'); + } + }, [trainingMode, addLog]); + + const startTraining = async () => { + if (trainingMode === 'cloud') { + startCloudTraining(); + return; + } + + if (sim.current.episodes.length === 0) return; + + sim.current.isTraining = true; + setIsTraining(true); + setTrainingProgress(0); + setTrainingStatus('Preparing data with Action Chunking...'); + + addLog(`Initializing ACT training (Chunk Size: ${actService.CHUNK_SIZE}, Max 300 frames)...`, 'info'); + + const trainingLog = (msg: string) => addLog(msg, 'info'); + + try { + // Prepare Data with Action Chunking (async to keep UI responsive) + trainingLog('Preparing training data...'); + const data = await actService.prepareTrainingData(sim.current.episodes); + + if (data.imageInputs.length === 0) { + addLog('No visual data found in episodes! Make sure the camera canvas is rendering.', 'error'); + return; + } + + if (data.totalFrames && data.usedFrames && data.totalFrames > data.usedFrames) { + addLog(`Subsampled ${data.totalFrames} frames to ${data.usedFrames} for training (memory limit).`, 'warning'); + } + trainingLog(`Training with ${data.imageInputs.length} frames.`); + + // Define ACT Model + trainingLog('Creating model (dense-only, no conv)...'); + const model = actService.createModel(); + sim.current.model = model; + + await actService.trainModel(model, data, (epoch, logs) => { + const progress = ((epoch + 1) / 20) * 100; + setTrainingProgress(progress); + setTrainingStatus(`Epoch ${epoch + 1}/20 - Loss: ${logs?.loss.toFixed(4)}`); + }, trainingLog); + + await finishTraining(); + } catch (err: any) { + addLog(`Training failed: ${err?.message || err}`, 'error'); + console.error('Training error:', err); + } finally { + sim.current.isTraining = false; + setIsTraining(false); + } + }; + + const runInference = useCallback(() => { + if (!sim.current.isInferencing || !sim.current.target) return; + + const { robotState, target, model } = sim.current; + + if (selectedModel === '跟随网球示例' || selectedModel === '自动避障示例') { + let targetSpeed = 0; + let targetTurn = 0; + + if (selectedModel === '跟随网球示例') { + const dirX = Math.sin(robotState.rotation); + const dirZ = Math.cos(robotState.rotation); + + // Calculate from camera position (0.85 units forward) to match visual FOV + const camX = robotState.x + dirX * 0.85; + const camZ = robotState.z + dirZ * 0.85; + + const dx = target.position.x - camX; + const dz = target.position.z - camZ; + const distance = Math.sqrt(dx * dx + dz * dz); + + const targetAngle = Math.atan2(dx, dz); + let angleDiff = targetAngle - robotState.rotation; + + while (angleDiff > Math.PI) angleDiff -= Math.PI * 2; + while (angleDiff < -Math.PI) angleDiff += Math.PI * 2; + + // Camera horizontal FOV is ~96 degrees (48 left/right). Use 45 degrees for strict visibility. + const FOV = Math.PI / 4; + let isVisible = Math.abs(angleDiff) <= FOV; + + // Check for obstacles blocking the view + if (isVisible && distance > 1.0) { + const steps = Math.floor(distance / 0.5); + const stepX = dx / steps; + const stepZ = dz / steps; + let px = camX; + let pz = camZ; + + for (let i = 1; i < steps; i++) { + px += stepX; + pz += stepZ; + for (const wall of sim.current.walls) { + if (wall === target) continue; + const w = wall.userData.w; + const depth = wall.userData.d; + const wx = wall.position.x; + const wz = wall.position.z; + if (px > wx - w/2 && px < wx + w/2 && pz > wz - depth/2 && pz < wz + depth/2) { + isVisible = false; + break; + } + } + if (!isVisible) break; + } + } + + if (distance <= 1.5 && isVisible) { + // Reached the ball and it's visible: stop completely, no jittering + targetSpeed = 0; + targetTurn = 0; + } else if (!isVisible) { + // Ball out of FOV or blocked: rotate in place to search + targetSpeed = 0; + targetTurn = turnSpeed; + } else { + // Ball in FOV: track and approach + // Use a smaller multiplier for smoother turning + targetTurn = angleDiff * 1.0; + + // Cap the turn speed to prevent violent swings + targetTurn = Math.max(-turnSpeed * 1.5, Math.min(turnSpeed * 1.5, targetTurn)); + + // Smooth speed approach + targetSpeed = Math.min(speed, (distance - 1.5) * 0.5); + + // If the angle is too large, slow down to turn + if (Math.abs(angleDiff) > Math.PI / 6) { + targetSpeed *= 0.5; + } + } + } else if (selectedModel === '自动避障示例') { + const checkDistance = (angleOffset: number) => { + const checkAngle = robotState.rotation + angleOffset; + const dirX = Math.sin(checkAngle); + const dirZ = Math.cos(checkAngle); + + let minDist = 5; // max lookahead + const robotRadius = 1.0; // slightly larger for safety + + for (const wall of sim.current.walls) { + for (let d = 0.5; d < minDist; d += 0.5) { + const px = robotState.x + dirX * d; + const pz = robotState.z + dirZ * d; + + const w = wall.userData.w; + const depth = wall.userData.d; + const wx = wall.position.x; + const wz = wall.position.z; + + if (px > wx - w/2 - robotRadius && px < wx + w/2 + robotRadius && + pz > wz - depth/2 - robotRadius && pz < wz + depth/2 + robotRadius) { + minDist = d; + break; + } + } + } + return minDist; + }; + + const distFront = checkDistance(0); + const distLeft = checkDistance(Math.PI / 4); + const distRight = checkDistance(-Math.PI / 4); + + targetSpeed = speed * 0.8; + targetTurn = 0; + + if (distFront < 3) { + targetSpeed = 0; + if (distLeft > distRight) { + targetTurn = turnSpeed; + } else { + targetTurn = -turnSpeed; + } + if (distLeft < 2 && distRight < 2) { + targetSpeed = -speed * 0.5; + targetTurn = turnSpeed; + } + } else { + if (distLeft < 3) targetTurn = -turnSpeed * 0.5; + if (distRight < 3) targetTurn = turnSpeed * 0.5; + } + } + + targetSpeed = Math.max(-speed, Math.min(speed, targetSpeed)); + targetTurn = Math.max(-turnSpeed, Math.min(turnSpeed, targetTurn)); + + robotState.velocity = robotState.velocity * 0.5 + targetSpeed * 0.5; + robotState.angularVelocity = robotState.angularVelocity * 0.5 + targetTurn * 0.5; + + const newActiveKeys: Record = {}; + if (robotState.velocity > 0.02) newActiveKeys['w'] = true; + if (robotState.velocity < -0.02) newActiveKeys['s'] = true; + if (robotState.angularVelocity > 0.01) newActiveKeys['a'] = true; + if (robotState.angularVelocity < -0.01) newActiveKeys['d'] = true; + + setActiveKeys(prev => { + const keysChanged = Object.keys(newActiveKeys).length !== Object.keys(prev).length || + Object.keys(newActiveKeys).some(k => newActiveKeys[k] !== prev[k]); + return keysChanged ? newActiveKeys : prev; + }); + + sim.current.inferenceTimeoutId = setTimeout(runInference, 100); + return; + } + + if (!model) return; + + // Stuck detection logic + if (typeof sim.current.lastX === 'undefined') { + sim.current.lastX = robotState.x; + sim.current.lastZ = robotState.z; + sim.current.stuckCounter = 0; + } + + const distMoved = Math.sqrt( + Math.pow(robotState.x - sim.current.lastX, 2) + + Math.pow(robotState.z - sim.current.lastZ, 2) + ); + sim.current.lastX = robotState.x; + sim.current.lastZ = robotState.z; + + if (Math.abs(robotState.velocity) > 0.05 && distMoved < 0.005) { + sim.current.stuckCounter++; + } else { + sim.current.stuckCounter = Math.max(0, sim.current.stuckCounter - 1); + } + + if (sim.current.stuckCounter > 20) { + addLog('Stuck detected! Recovering...', 'warning'); + robotState.velocity = -0.2; + robotState.angularVelocity = (Math.random() - 0.5) * 2.0; // Increased rotation range + sim.current.stuckCounter = 0; + // Increase recovery time to 1.5s to allow robot to back away fully + sim.current.inferenceTimeoutId = setTimeout(runInference, 1500); + return; + } + + const image = captureImage(); + if (!image) return; + + const dx = target.position.x - robotState.x; + const dz = target.position.z - robotState.z; + const targetDist = Math.sqrt(dx * dx + dz * dz); + + const state = new Array(14).fill(0); + state[0] = robotState.x; + state[1] = robotState.z; + state[2] = robotState.rotation; + state[3] = robotState.velocity; + state[4] = targetDist; + + const prediction = actService.predict(model, image, state); + + // Temporal Ensembling (simplified) + if (!sim.current.actionBuffer) sim.current.actionBuffer = []; + + // Parse prediction into chunk + const newChunk = []; + for (let i = 0; i < actService.CHUNK_SIZE; i++) { + newChunk.push({ + v: prediction[i * 2], + w: prediction[i * 2 + 1], + weight: Math.exp(-0.5 * i) // Exponential weighting + }); + } + setActionChunks(newChunk.map(a => a.v)); + sim.current.actionBuffer.push(newChunk); + + // Keep buffer size limited + if (sim.current.actionBuffer.length > actService.CHUNK_SIZE) { + sim.current.actionBuffer.shift(); + } + + // Aggregate actions + let sumV = 0, sumW = 0, totalWeight = 0; + + sim.current.actionBuffer.forEach((chunk: any[], index: number) => { + const offset = sim.current.actionBuffer.length - 1 - index; + if (offset < chunk.length) { + const action = chunk[offset]; + sumV += action.v * action.weight; + sumW += action.w * action.weight; + totalWeight += action.weight; + } + }); + + if (totalWeight > 0) { + // Apply slight smoothing to velocity to prevent sudden jumps + const targetV = Math.max(-speed, Math.min(speed, sumV / totalWeight)); + const targetW = Math.max(-turnSpeed, Math.min(turnSpeed, sumW / totalWeight)); + + // Simple low-pass filter (alpha = 0.5) + robotState.velocity = robotState.velocity * 0.5 + targetV * 0.5; + robotState.angularVelocity = robotState.angularVelocity * 0.5 + targetW * 0.5; + + // Log movement periodically (every 200ms) + if (!sim.current.lastInferenceLogTime || Date.now() - sim.current.lastInferenceLogTime > 200) { + addLog(`Inference Movement: V=${robotState.velocity.toFixed(2)}, W=${robotState.angularVelocity.toFixed(2)}`, 'info'); + sim.current.lastInferenceLogTime = Date.now(); + } + } else { + robotState.velocity = Math.max(-speed, Math.min(speed, prediction[0])); + robotState.angularVelocity = Math.max(-turnSpeed, Math.min(turnSpeed, prediction[1])); + } + + // Update active keys for UI highlighting during inference + const newActiveKeys: Record = {}; + if (robotState.velocity > 0.02) newActiveKeys['w'] = true; + if (robotState.velocity < -0.02) newActiveKeys['s'] = true; + if (robotState.angularVelocity > 0.01) newActiveKeys['a'] = true; + if (robotState.angularVelocity < -0.01) newActiveKeys['d'] = true; + + setActiveKeys(prev => { + const keysChanged = Object.keys(newActiveKeys).length !== Object.keys(prev).length || + Object.keys(newActiveKeys).some(k => newActiveKeys[k] !== prev[k]); + return keysChanged ? newActiveKeys : prev; + }); + + // Check goal + if (targetDist < 1.0) { + addLog('Target reached!', 'success'); + target.position.set( + (Math.random() - 0.5) * 12, + 0.4, + (Math.random() - 0.5) * 12 + ); + sim.current.actionBuffer = []; + } + + // Match training frequency (10Hz = 100ms) + sim.current.inferenceTimeoutId = setTimeout(runInference, 100); + }, [addLog, captureImage, speed, turnSpeed, selectedModel]); + + const startInference = async () => { + if (isInferencing) { + stopInference(); + return; + } + + if (trainingMode === 'cloud') { + startCloudInference(); + } else { + if (!selectedModel) { + addLog('No frontend model selected!', 'error'); + return; + } + + try { + if (selectedModel === '跟随网球示例' || selectedModel === '自动避障示例') { + addLog(`Loading ${selectedModel}...`, 'info'); + sim.current.isInferencing = true; + setIsInferencing(true); + addLog(`Starting Frontend ACT inference with ${selectedModel}...`, 'success'); + runInference(); + } else { + addLog(`Loading model ${selectedModel}...`, 'info'); + sim.current.model = await tf.loadLayersModel(`indexeddb://${selectedModel}`); + sim.current.isInferencing = true; + setIsInferencing(true); + addLog(`Starting Frontend ACT inference with ${selectedModel}...`, 'success'); + runInference(); + } + } catch (err) { + console.error(err); + addLog(`Failed to load model ${selectedModel}`, 'error'); + } + } + }; + + const [showManual, setShowManual] = useState(false); + const [manualPage, setManualPage] = useState(1); + + return ( +
+ {showManual && ( +
setShowManual(false)}> +
e.stopPropagation()}> + + +
+

+ {manualPage === 1 ? '训练手册 - 操作指南' : '训练手册 - 技术文档'} +

+
+ + +
+
+ +
+ {manualPage === 1 ? ( + <> +
+

1. 快速开始

+

本模拟器允许您通过“动作分块（ACT）”技术训练小车自动导航。基本流程如下：

+
    +
  • 手动控制：使用键盘 WASD 或方向键控制小车移动。
    • +
  • 数据采集：点击“开始采集”，在手动控制的同时记录小车的行为。
    • +
  • 模型训练：采集足够数据后（建议至少 10 个 Episode），点击“开始训练”。
    • +
  • 自主推理：训练完成后，在下拉列表中选择模型，点击“启动自主推理”。
    • +
+
+
+

2. 训练要求

+
    +
  • 多样性：从不同位置开始，以不同角度接近目标。
    • +
  • 平滑性：手动操作时尽量保持平滑，避免剧烈的无意义转向。
    • +
  • 数据量：建议每个场景采集 15-20 个成功的 Episode。
    • +
+
+
+

3. 训练技巧

+
    +
  • 碰撞保护：开启碰撞保护可以自动过滤掉撞墙的无效数据。
    • +
  • 速度设置：训练时的速度设置会影响模型的学习。建议先用低速采集，模型稳定后再尝试高速。
    • +
  • 目标距离：小车距离目标越近，采集到的“冲刺”数据越精准。
    • +
+
+ + ) : ( + <> +
+

1. 系统架构

+

本系统基于 React 和 Three.js 构建，采用前端本地训练与推理架构。核心组件包括：

+
    +
  • Three.js 场景渲染：负责小车、环境、光源和机载摄像头的 3D 渲染。
    • +
  • TensorFlow.js 模型引擎：在浏览器端执行 ACT (Action Chunking with Transformers) 模型的训练与推理。
    • +
  • 状态管理：使用 React Hooks 和 `useRef` 管理仿真状态（机器人位置、速度、动作缓冲区）。
    • +
+
+
+

2. ACT 原理与实现

+

ACT (Action Chunking with Transformers) 旨在解决机器人动作预测中的平滑性与多模态问题。

+
    +
  • 动作分块 (Action Chunking)：模型一次预测未来多个时间步的动作序列，而非单一动作，从而显著降低动作抖动。
    • +
  • Transformer 架构：利用自注意力机制捕捉动作序列的时序依赖。
    • +
  • CVAE (条件变分自编码器)：在训练时建模动作的多模态分布，推理时通过采样实现动作的多样性。
    • +
+
+
+

3. 训练与推理细节

+

整个生命周期分为以下阶段：

+
    +
  • 数据预处理：将图像缩放至 224x224，并对状态向量进行归一化。
    • +
  • 损失函数：结合 L1 动作损失和 KL 散度（用于 CVAE 潜空间正则化）。
    • +
  • 时间集成 (Temporal Ensembling)：在推理时，对重叠的动作块进行加权平均，进一步提升平滑度。
    • +
+
+ + )} +
+ +
+ 页码: {manualPage} / 2 +
+ {manualPage === 1 ? ( + + ) : ( + + )} + +
+
+
+
+ )} +
+
+
🤖
+
+

AKA ACT Simulator

+

Action Chunking with Transformers - Educational Edition

+
+
+
+ +
+ + Simulation Active +
+
60 FPS
+
+
+ +
+ + +
+
+ +
+
Position: 0.00, 0.00
+
Rotation: 0°
+
Velocity: 0.0 m/s
+
+ +
+ {isTraining ? '训练中...' : isInferencing ? 'ACT 自主推理中' : trainedModel && !isInferencing ? '训练完成' : '手动控制模式'} +
+
+ + +
+
+ ); +} \ No newline at end of file diff --git a/frontend/src/components/ControlButton.tsx b/frontend/src/components/ControlButton.tsx index faef0602..d9f9c99f 100644 --- a/frontend/src/components/ControlButton.tsx +++ b/frontend/src/components/ControlButton.tsx @@ -53,7 +53,7 @@ const ControlButton = ({ onPointerUp={handlePressEnd} onPointerLeave={handlePressEnd} onClick={(e) => { - e.preventDefault(); // 防止默认行为（如表单提交） + e.preventDefault(); onClick?.(); }} style={{ @@ -66,7 +66,7 @@ const ControlButton = ({ fontSize: "16px", transition: "all 0.15s ease", boxShadow: isPressed - ? "0 3px 8px rgba(0,0,0,0.4)" // 按下时阴影变浅/变小 + ? "0 3px 8px rgba(0,0,0,0.4)" : "0 6px 15px rgba(0,0,0,0.5)", transform: isPressed ? "scale(0.96)" : "scale(1)", touchAction: "none", @@ -77,4 +77,4 @@ const ControlButton = ({ ) } -export default ControlButton \ No newline at end of file +export default ControlButton; diff --git a/frontend/src/components/Header.tsx b/frontend/src/components/Header.tsx new file mode 100644 index 00000000..c3171b7f --- /dev/null +++ b/frontend/src/components/Header.tsx @@ -0,0 +1,22 @@ +import React from 'react'; + +export const Header: React.FC = () => { + return ( +
+
+
🤖
+
+

LeRobot ACT Simulator

+

Action Chunking with Transformers - Educational Edition

+
+
+
+
+ + Simulation Active +
+
60 FPS
+
+
+ ); +}; diff --git a/frontend/src/components/SidebarLeft.tsx b/frontend/src/components/SidebarLeft.tsx new file mode 100644 index 00000000..05fb7b50 --- /dev/null +++ b/frontend/src/components/SidebarLeft.tsx @@ -0,0 +1,371 @@ +import React from 'react'; +import { CloudDataset, CloudModel, CloudTrainingStatus } from '../types'; + +interface SidebarLeftProps { + trainingMode: 'frontend' | 'cloud'; + setTrainingMode: (mode: 'frontend' | 'cloud') => void; + + // Cloud Props + cloudDatasets: CloudDataset[]; + selectedCloudDataset: string; + setSelectedCloudDataset: (id: string) => void; + fetchCloudDatasets: () => void; + cloudModels: CloudModel[]; + selectedCloudModel: string; + setSelectedCloudModel: (id: string) => void; + fetchCloudModels: () => void; + cloudTrainingStatus: CloudTrainingStatus | null; + + // Scene Props + sceneType: string; + setSceneType: (type: string) => void; + sceneSize: string; + setSceneSize: (size: string) => void; + sceneComplexity: string; + setSceneComplexity: (complexity: string) => void; + + // Robot Props + robotConfig: string[]; + setRobotConfig: (config: string[]) => void; + lightPos: { x: number, y: number, z: number }; + setLightPos: (pos: { x: number, y: number, z: number }) => void; + resetRobot: () => void; + + // Control Props + speed: number; + setSpeed: (speed: number) => void; + turnSpeed: number; + setTurnSpeed: (speed: number) => void; + simRef: React.MutableRefObject; // Using any for simplicity here, but could be typed + sendCommand: (cmd: string) => void; + + // Recording Props + isRecording: boolean; + toggleRecording: () => void; + episodesCount: number; + frameCount: number; + actionCount: number; + saveDataset: () => void; + + // Training Props + startTraining: () => void; + isTraining: boolean; + trainingProgress: number; + trainingStatus: string; + + // Inference Props + trainedModel: { name: string } | null; + startInference: () => void; + isInferencing: boolean; + showAttention: boolean; + setShowAttention: (show: boolean) => void; +} + +export const SidebarLeft: React.FC = ({ + trainingMode, setTrainingMode, + cloudDatasets, selectedCloudDataset, setSelectedCloudDataset, fetchCloudDatasets, + cloudModels, selectedCloudModel, setSelectedCloudModel, fetchCloudModels, cloudTrainingStatus, + sceneType, setSceneType, sceneSize, setSceneSize, sceneComplexity, setSceneComplexity, + robotConfig, setRobotConfig, lightPos, setLightPos, resetRobot, + speed, setSpeed, turnSpeed, setTurnSpeed, simRef, sendCommand, + isRecording, toggleRecording, episodesCount, frameCount, actionCount, saveDataset, + startTraining, isTraining, trainingProgress, trainingStatus, + trainedModel, startInference, isInferencing, showAttention, setShowAttention +}) => { + return ( + + ); +}; diff --git a/frontend/src/components/SidebarRight.tsx b/frontend/src/components/SidebarRight.tsx new file mode 100644 index 00000000..ae0811a0 --- /dev/null +++ b/frontend/src/components/SidebarRight.tsx @@ -0,0 +1,64 @@ +import React from 'react'; + +interface SidebarRightProps { + cameraCanvasRef: React.RefObject; + showAttention: boolean; + actionChunks: number[]; + logs: { message: string, type: string, time: string }[]; + logContainerRef: React.RefObject; + clearLogs: () => void; +} + +export const SidebarRight: React.FC = ({ + cameraCanvasRef, + showAttention, + actionChunks, + logs, + logContainerRef, + clearLogs +}) => { + return ( + + ); +}; diff --git a/frontend/src/control-main.tsx b/frontend/src/control-main.tsx new file mode 100644 index 00000000..b6884249 --- /dev/null +++ b/frontend/src/control-main.tsx @@ -0,0 +1,9 @@ +import {StrictMode} from 'react'; +import {createRoot} from 'react-dom/client'; +import BaseControlPage from './pages/BaseControlPage.tsx'; + +createRoot(document.getElementById('root')!).render( + + + , +); diff --git a/frontend/src/index.css b/frontend/src/index.css index 5f0b13af..a26a70a3 100644 --- a/frontend/src/index.css +++ b/frontend/src/index.css @@ -1,79 +1,159 @@ -:root { - font-family: system-ui, Avenir, Helvetica, Arial, sans-serif; - line-height: 1.5; - font-weight: 400; +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;700&display=swap'); +@import "tailwindcss"; - color-scheme: light dark; - color: rgba(255, 255, 255, 0.87); - background-color: #242424; +@theme { + --font-sans: "Inter", ui-sans-serif, system-ui, sans-serif; + --font-mono: "JetBrains Mono", ui-monospace, SFMono-Regular, monospace; +} - font-synthesis: none; - text-rendering: optimizeLegibility; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; +body { + overflow-x: hidden; +} - -webkit-touch-callout: none !important; /* 禁用 iOS 长按弹出“拷贝” */ - -webkit-user-select: none !important; /* 禁用文本选择 */ - -moz-user-select: none !important; - -ms-user-select: none !important; - user-select: none !important; +.mono { + font-family: var(--font-mono); +} - -webkit-tap-highlight-color: transparent !important; /* 去掉点击高亮灰块（iOS/Android）*/ - -webkit-focus-ring-color: transparent !important; - outline: none !important; +.glass-panel { + background: rgba(20, 20, 30, 0.8); + backdrop-filter: blur(12px); + border: 1px solid rgba(100, 100, 255, 0.1); + box-shadow: 0 8px 32px rgba(0, 0, 0, 0.4); } -a { - font-weight: 500; - color: #646cff; - text-decoration: inherit; +.neon-border { + border: 1px solid rgba(56, 189, 248, 0.3); + box-shadow: 0 0 20px rgba(56, 189, 248, 0.1); } -a:hover { - color: #535bf2; +.camera-feed { + background: #000; + position: relative; + overflow: hidden; } -body { +.camera-feed::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: linear-gradient(45deg, transparent 48%, rgba(56, 189, 248, 0.03) 50%, transparent 52%); + background-size: 4px 4px; + pointer-events: none; + z-index: 10; +} + +.recording-pulse { + animation: pulse-red 1.5s infinite; +} + +@keyframes pulse-red { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.3; } +} + +.training-bar { + background: linear-gradient(90deg, #3b82f6 0%, #8b5cf6 100%); + transition: width 0.3s ease; +} + +.attention-heatmap { + background: radial-gradient(circle at center, rgba(239, 68, 68, 0.6) 0%, transparent 70%); + mix-blend-mode: overlay; +} + +.grid-bg { + background-image: + linear-gradient(rgba(56, 189, 248, 0.05) 1px, transparent 1px), + linear-gradient(90deg, rgba(56, 189, 248, 0.05) 1px, transparent 1px); + background-size: 20px 20px; +} + +.control-btn { + transition: all 0.2s; + position: relative; + overflow: hidden; +} +.control-btn:active { + transform: scale(0.95); } -h1 { - font-size: 3.2em; - line-height: 1.1; +.control-btn::after { + content: ''; + position: absolute; + top: 50%; + left: 50%; + width: 0; + height: 0; + border-radius: 50%; + background: rgba(255,255,255,0.2); + transform: translate(-50%, -50%); + transition: width 0.3s, height 0.3s; } -button { - border-radius: 8px; - border: 1px solid transparent; - padding: 0.6em 1.2em; - font-size: 1em; - font-weight: 500; - font-family: inherit; - background-color: #1a1a1a; - cursor: pointer; - transition: border-color 0.25s; +.control-btn:active::after { + width: 200px; + height: 200px; } -button:hover { - border-color: #646cff; +.log-entry { + border-left: 2px solid transparent; + padding-left: 8px; + margin: 2px 0; + font-size: 0.7rem; + animation: slideIn 0.3s ease; } -button:focus, -button:focus-visible { - outline: 4px auto -webkit-focus-ring-color; +@keyframes slideIn { + from { opacity: 0; transform: translateX(-10px); } + to { opacity: 1; transform: translateX(0); } } -@media (prefers-color-scheme: light) { - :root { - color: #213547; - background-color: #ffffff; - } +.log-info { border-left-color: #3b82f6; color: #93c5fd; } +.log-success { border-left-color: #10b981; color: #6ee7b7; } +.log-warning { border-left-color: #f59e0b; color: #fcd34d; } +.log-error { border-left-color: #ef4444; color: #fca5a5; } - a:hover { - color: #747bff; - } +/* ====================== 极简暗色滚动条（Chrome/Edge/Safari） ====================== */ +/* 滚动条整体尺寸 */ +::-webkit-scrollbar { + width: 4px; /* 垂直滚动条：极细 */ + height: 4px; /* 水平滚动条：极细 */ +} - button { - background-color: #f9f9f9; - } +/* 隐藏滑轨/轨道（无背景） */ +::-webkit-scrollbar-track { + background: transparent; /* 完全透明，看不见滑轨 */ } + +/* 滑块：纯深色、极简 */ +::-webkit-scrollbar-thumb { + background: #5f82aa; /* 深灰色滑块（暗色模式） */ + border-radius: 2px; /* 极细微圆角，更精致 */ +} + +/* 悬浮加深色，更有质感 */ +::-webkit-scrollbar-thumb:hover { + background: #222; +} + +/* 隐藏滚动条按钮 */ +::-webkit-scrollbar-button { + display: none; +} + +/* ====================== 极简暗色滚动条（Firefox） ====================== */ +* { + scrollbar-width: thin; /* 细滚动条 */ + scrollbar-color: #5f82aa transparent; /* 滑块深色 + 滑轨透明 */ +} + + + + + + + diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx index fd2cc0a0..080dac37 100644 --- a/frontend/src/main.tsx +++ b/frontend/src/main.tsx @@ -1,10 +1,10 @@ -import { createRoot } from 'react-dom/client' -import './index.css' -import App from './App.tsx' -import {BrowserRouter} from "react-router-dom"; +import {StrictMode} from 'react'; +import {createRoot} from 'react-dom/client'; +import App from './App.tsx'; +import './index.css'; createRoot(document.getElementById('root')!).render( - + - , -) + , +); diff --git a/frontend/src/pages/BaseControlPage.tsx b/frontend/src/pages/BaseControlPage.tsx index b704021e..24c05557 100644 --- a/frontend/src/pages/BaseControlPage.tsx +++ b/frontend/src/pages/BaseControlPage.tsx @@ -1,6 +1,6 @@ import {useEffect, useRef, useState} from "react"; -import {sendAction} from "../api/socket.ts"; -import ControlButton from "../components/ControlButton.tsx"; +import {sendAction} from "../api/socket"; +import ControlButton from "../components/ControlButton"; const FPS = 20 const frameInterval = 1000 / FPS @@ -10,8 +10,6 @@ const BaseControlPage = () => { const [status, setStatus] = useState("准备就绪"); const [isSimulator, setIsSimulator] = useState(false); const [targetIp, setTargetIp] = useState(""); - - // 当前正在执行的动作（用于模拟器每帧发送） const currentActionRef = useRef(null); useEffect(() => { @@ -68,23 +66,22 @@ const BaseControlPage = () => { } }; - // ==== 按钮事件处理 ==== const handlePressStart = (action: string) => { currentActionRef.current = action; if (!isSimulator) { - send(action); // 实车立即发 + send(action); } }; const handlePressEnd = () => { currentActionRef.current = null; if (!isSimulator) { - send("stop"); // 实车发 stop + send("stop"); } }; useEffect(() => { - if (!isSimulator) return; // 只在模拟器模式运行 + if (!isSimulator) return; let animationFrameId: number let lastTime = 0; const renderLoop = (currentTime: number) => { @@ -97,7 +94,7 @@ const BaseControlPage = () => { lastTime = currentTime - (delta % frameInterval) if (action !== null) { - sendAction(action); // 每帧发送当前动作 + sendAction(action); } }; @@ -143,7 +140,6 @@ const BaseControlPage = () => {

AKA-00 控制台

{ip}
- {/* 模式状态 */}
{
- {/* 目标IP输入 */} {!isSimulator && (
{
)} - {/* 方向区 */}
{
- {/* 功能按钮 */}
{ flexWrap: "wrap", }} > - {/* 跳转 */}
{
- {/* 切换 */}
{ 切换模式
+ +
+ window.location.href = '/sim3d'} + > + 3D 模拟器 + +
diff --git a/frontend/src/services/actService.ts b/frontend/src/services/actService.ts new file mode 100644 index 00000000..195e77b5 --- /dev/null +++ b/frontend/src/services/actService.ts @@ -0,0 +1,181 @@ +import * as tf from '@tensorflow/tfjs'; +import { Episode } from '../types'; + +export const CHUNK_SIZE = 10; + +/** Maximum number of frames to use for training (prevent browser OOM) */ +const MAX_TRAINING_FRAMES = 300; + +/** Delay helper to yield to the event loop */ +const tick = () => new Promise(resolve => setTimeout(resolve, 0)); + +export const actService = { + CHUNK_SIZE, + + async prepareTrainingData(episodes: Episode[]) { + const allItems: { image: number[][][], state: number[], chunk: number[] }[] = []; + + // O(n) single pass through all episodes + for (const ep of episodes) { + for (let i = 0; i < ep.length; i++) { + const frame = ep[i]; + if (!frame.image) continue; + + const chunk: number[] = []; + for (let k = 0; k < CHUNK_SIZE; k++) { + const futureIndex = Math.min(i + k, ep.length - 1); + const futureFrame = ep[futureIndex]; + const action = futureFrame?.action; + chunk.push(typeof action?.[0] === 'number' ? action[0] : 0); + chunk.push(typeof action?.[1] === 'number' ? action[1] : 0); + } + + allItems.push({ + image: frame.image, + state: frame.state, + chunk, + }); + } + } + + // Yield to keep UI responsive during data prep + await tick(); + + const totalFrames = allItems.length; + + // Subsample if too many frames + let items = allItems; + if (totalFrames > MAX_TRAINING_FRAMES) { + const step = totalFrames / MAX_TRAINING_FRAMES; + items = []; + for (let i = 0; i < MAX_TRAINING_FRAMES; i++) { + items.push(allItems[Math.floor(i * step)]); + } + } + + const imageInputs = items.map(it => it.image); + const stateInputs = items.map(it => it.state); + const labelChunks = items.map(it => it.chunk); + + return { imageInputs, stateInputs, labelChunks, totalFrames, usedFrames: items.length }; + }, + + createModel() { + // Simple dense-only model - much faster than conv2d in browser, + // avoids WebGL shader compilation freeze + const imageInput = tf.input({ shape: [64 * 64 * 3] }); // flattened image + const stateInput = tf.input({ shape: [14] }); + + const i1 = tf.layers.dense({ units: 64, activation: 'relu' }).apply(imageInput) as tf.SymbolicTensor; + const s1 = tf.layers.dense({ units: 32, activation: 'relu' }).apply(stateInput) as tf.SymbolicTensor; + + const concatenated = tf.layers.concatenate().apply([i1, s1]) as tf.SymbolicTensor; + + const d1 = tf.layers.dense({ units: 128, activation: 'relu' }).apply(concatenated) as tf.SymbolicTensor; + const d2 = tf.layers.dense({ units: 64, activation: 'relu' }).apply(d1) as tf.SymbolicTensor; + const output = tf.layers.dense({ units: CHUNK_SIZE * 2, activation: 'linear' }).apply(d2) as tf.SymbolicTensor; + + const model = tf.model({ inputs: [imageInput, stateInput], outputs: output }); + model.compile({ optimizer: 'adam', loss: 'meanSquaredError' }); + + return model; + }, + + async trainModel( + model: tf.LayersModel, + data: { imageInputs: number[][][][], stateInputs: number[][], labelChunks: number[][], totalFrames?: number, usedFrames?: number }, + onEpochEnd: (epoch: number, logs: tf.Logs | undefined) => void, + onLog: (msg: string) => void + ) { + // Save current backend and switch to CPU to avoid WebGL shader compilation freeze + const prevBackend = tf.getBackend(); + onLog(`Current TF.js backend: ${prevBackend}, switching to 'cpu' for training...`); + await tf.setBackend('cpu'); + await tf.ready(); + onLog('CPU backend ready.'); + + await tick(); // yield before heavy computation + + // Flatten images for the dense-only model input: [N, 64, 64, 3] -> [N, 64*64*3] + const flatImages: number[][] = data.imageInputs.map(img => { + const flat: number[] = []; + for (let y = 0; y < 64; y++) { + for (let x = 0; x < 64; x++) { + flat.push(img[y][x][0], img[y][x][1], img[y][x][2]); + } + } + return flat; + }); + + onLog(`Flattened ${flatImages.length} images to ${flatImages[0].length}-dim vectors.`); + await tick(); + + let xsImage: tf.Tensor2D | null = null; + let xsState: tf.Tensor2D | null = null; + let ys: tf.Tensor2D | null = null; + + try { + onLog('Creating tensors...'); + xsImage = tf.tensor2d(flatImages); + xsState = tf.tensor2d(data.stateInputs); + ys = tf.tensor2d(data.labelChunks); + + await tick(); + onLog('Tensors created. Starting model.fit()...'); + + const totalEpochs = 20; + const batchSize = 16; + + await model.fit([xsImage, xsState], ys, { + epochs: totalEpochs, + batchSize, + shuffle: true, + callbacks: { + onEpochEnd: (epoch, logs) => { + onEpochEnd(epoch, logs); + // Yield after each epoch to keep UI responsive + return new Promise(resolve => setTimeout(resolve, 0)); + }, + onBatchEnd: (batch) => { + // Yield every batch to prevent UI freeze on CPU backend + if (batch % 5 === 0) { + return new Promise(resolve => setTimeout(resolve, 0)); + } + } + } + }); + + onLog('Training complete!'); + } finally { + if (xsImage) xsImage.dispose(); + if (xsState) xsState.dispose(); + if (ys) ys.dispose(); + + // Restore previous backend (WebGL) for inference + try { + await tf.setBackend(prevBackend); + await tf.ready(); + onLog(`Restored backend to: ${tf.getBackend()}`); + } catch { + onLog('Could not restore WebGL backend, staying on CPU.'); + } + } + }, + + predict(model: tf.LayersModel, image: number[][][], state: number[]) { + return tf.tidy(() => { + // Flatten image to match model input shape [64*64*3] + const flatImage: number[] = []; + for (let y = 0; y < 64; y++) { + for (let x = 0; x < 64; x++) { + flatImage.push(image[y][x][0], image[y][x][1], image[y][x][2]); + } + } + + const imageTensor = tf.tensor2d([flatImage]); + const stateTensor = tf.tensor2d([state]); + const prediction = model.predict([imageTensor, stateTensor]) as tf.Tensor; + return prediction.dataSync(); + }); + } +}; diff --git a/frontend/src/services/cloudService.ts b/frontend/src/services/cloudService.ts new file mode 100644 index 00000000..6e7b3b91 --- /dev/null +++ b/frontend/src/services/cloudService.ts @@ -0,0 +1,118 @@ +import { CloudModel, CloudDataset, CloudTrainingStatus } from '../types'; + +const baseUrl = ""; + +export const cloudService = { + async fetchModels(): Promise { + try { + const res = await fetch(baseUrl + '/api/models', { mode: 'cors' }); + const data = await res.json(); + return data.models || []; + } catch (e) { + console.error('Failed to fetch models', e); + return []; + } + }, + + async fetchDatasets(): Promise { + try { + const res = await fetch(baseUrl + '/api/datasets', { mode: 'cors' }); + const data = await res.json(); + return data.datasets || []; + } catch (e) { + console.error('Failed to fetch datasets', e); + return []; + } + }, + + async saveDataset(dataset: any): Promise { + try { + const res = await fetch(baseUrl + '/api/dataset', { + method: 'POST', + mode: 'cors', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(dataset) + }); + const data = await res.json(); + return data.status === 'success'; + } catch (e) { + console.error('Failed to upload dataset', e); + return false; + } + }, + + async startTraining(datasetPath: string): Promise { + try { + const res = await fetch(baseUrl + '/api/train/start', { + method: 'POST', + mode: 'cors', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + dataset_path: datasetPath + }) + }); + const data = await res.json(); + return res.ok; + } catch (e) { + console.error('Failed to start cloud training', e); + return false; + } + }, + + async getTrainingStatus(): Promise { + try { + const res = await fetch(baseUrl + '/api/train/status', { mode: 'cors' }); + const data = await res.json(); + return data; + } catch (e) { + return null; + } + }, + + async startInference(modelId: string): Promise { + try { + const res = await fetch(baseUrl + '/api/infer/start', { + method: 'POST', + mode: 'cors', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ model_id: modelId }) + }); + const data = await res.json(); + return res.ok; + } catch (e) { + console.error('Failed to start cloud inference', e); + return false; + } + }, + + async stopInference(): Promise { + try { + await fetch(baseUrl + '/api/infer/stop', { method: 'POST', mode: 'cors' }); + return true; + } catch (e) { + return false; + } + }, + + async runInferenceStep(state: number[], envState: number[]): Promise { + try { + const res = await fetch(baseUrl + '/api/infer/step', { + method: 'POST', + mode: 'cors', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + state: state, + env_state: envState + }) + }); + const data = await res.json(); + if (data.action) { + return data.action; + } + return null; + } catch (e) { + console.error('Cloud inference step failed', e); + return null; + } + } +}; diff --git a/frontend/src/sim/environment.ts b/frontend/src/sim/environment.ts new file mode 100644 index 00000000..6d71deeb --- /dev/null +++ b/frontend/src/sim/environment.ts @@ -0,0 +1,97 @@ +import * as THREE from 'three'; + +export function updateEnvironment( + scene: THREE.Scene, + sceneType: string, + sceneSize: string, + sceneComplexity: string, + currentWalls: THREE.Mesh[], + currentEnvironmentGroup: THREE.Group | null, + currentTarget: THREE.Mesh | null +) { + // Clean up + currentWalls.forEach(wall => scene.remove(wall)); + if (currentEnvironmentGroup) scene.remove(currentEnvironmentGroup); + if (currentTarget) scene.remove(currentTarget); + + const walls: THREE.Mesh[] = []; + const group = new THREE.Group(); + scene.add(group); + + let sizeVal = 20; + if (sceneSize === 'small') sizeVal = 10; + if (sceneSize === 'large') sizeVal = 30; + + let numObstacles = 5; + if (sceneComplexity === 'low') numObstacles = 2; + if (sceneComplexity === 'high') numObstacles = 10; + + const wallMat = new THREE.MeshStandardMaterial({ color: 0x475569 }); + const woodTex = new THREE.MeshStandardMaterial({ color: 0x8b5a2b }); + + const addWall = (x: number, z: number, w: number, h: number, d: number, color: number, mat?: THREE.Material) => { + const geo = new THREE.BoxGeometry(w, h, d); + const m = mat || new THREE.MeshStandardMaterial({ color }); + const mesh = new THREE.Mesh(geo, m); + mesh.position.set(x, h/2, z); + mesh.castShadow = true; + mesh.receiveShadow = true; + mesh.userData = { w, d }; + group.add(mesh); + walls.push(mesh); + }; + + // Boundary Walls + const halfSize = sizeVal / 2; + addWall(0, -halfSize, sizeVal, 2, 1, 0x1e293b); + addWall(0, halfSize, sizeVal, 2, 1, 0x1e293b); + addWall(-halfSize, 0, 1, 2, sizeVal, 0x1e293b); + addWall(halfSize, 0, 1, 2, sizeVal, 0x1e293b); + + if (sceneType === 'living_room') { + // Sofa + addWall(-halfSize + 3, -halfSize + 3, 4, 1, 2, 0x5c5c5c); + // Table + addWall(0, 0, 3, 0.8, 2, 0x8b5a2b, woodTex); + // TV Stand + addWall(halfSize - 2, 0, 1, 1.2, 4, 0x1e1e1e); + + for(let i=0; i { + const wheelGroup = new THREE.Group(); + wheelGroup.position.set(pos.x, 0.3, pos.z); + wheelGroup.rotation.z = Math.PI / 2; + + const wheel = new THREE.Mesh(wheelGeo, wheelMat); + wheel.castShadow = true; + wheelGroup.add(wheel); + + const spoke1 = new THREE.Mesh(spokeGeo, spokeMat); + const spoke2 = new THREE.Mesh(spokeGeo, spokeMat); + spoke2.rotation.y = Math.PI / 2; + + wheelGroup.add(spoke1); + wheelGroup.add(spoke2); + + robot.add(wheelGroup); + wheelMeshes.push(wheelGroup); + }); + + // Head/Camera Mount + const headGeo = new THREE.BoxGeometry(0.4, 0.4, 0.4); + const headMat = new THREE.MeshStandardMaterial({ color: 0x64748b }); + const head = new THREE.Mesh(headGeo, headMat); + head.position.set(0, 0.8, 0.6); + head.castShadow = true; + robot.add(head); + + // Camera Lens + const lensGeo = new THREE.CylinderGeometry(0.1, 0.1, 0.1, 16); + const lensMat = new THREE.MeshStandardMaterial({ color: 0x000000 }); + const lens = new THREE.Mesh(lensGeo, lensMat); + lens.rotation.x = Math.PI / 2; + lens.position.set(0, 0.8, 0.85); + robot.add(lens); + + // Onboard Camera + const onboardCamera = new THREE.PerspectiveCamera(80, 320/240, 0.1, 50); + onboardCamera.position.set(0, 0.7, 0.6); + + const onboardRenderTarget = new THREE.WebGLRenderTarget(320, 240); + + // Robotic Arm + const armGroup = new THREE.Group(); + armGroup.position.set(0, 0.6, 0.2); // Base of the arm on the robot body (moved forward) + + const orangeMat = new THREE.MeshStandardMaterial({ color: 0xff5722, roughness: 0.6 }); + const blackMat = new THREE.MeshStandardMaterial({ color: 0x1a1a1a, roughness: 0.8 }); + + // Base pillar (Black) + const basePillar = new THREE.Mesh(new THREE.BoxGeometry(0.4, 0.5, 0.4), blackMat); + basePillar.position.y = 0.25; + basePillar.castShadow = true; + armGroup.add(basePillar); + + // Shoulder (Orange) + const shoulder = new THREE.Group(); + shoulder.position.y = 0.5; + armGroup.add(shoulder); + + const shoulderBracket = new THREE.Mesh(new THREE.BoxGeometry(0.3, 0.3, 0.3), orangeMat); + shoulderBracket.position.y = 0.15; + shoulderBracket.castShadow = true; + shoulder.add(shoulderBracket); + + // Lower Arm + const lowerArm = new THREE.Group(); + lowerArm.position.y = 0.3; + lowerArm.rotation.x = Math.PI / 4; // Lean forward + shoulder.add(lowerArm); + + const lowerArmMesh = new THREE.Mesh(new THREE.BoxGeometry(0.2, 0.8, 0.2), orangeMat); + lowerArmMesh.position.y = 0.4; + lowerArmMesh.castShadow = true; + lowerArm.add(lowerArmMesh); + + // Elbow + const elbow = new THREE.Group(); + elbow.position.y = 0.8; + elbow.rotation.x = Math.PI / 2.5; // Bend down a bit + lowerArm.add(elbow); + + const elbowBracket = new THREE.Mesh(new THREE.BoxGeometry(0.3, 0.25, 0.25), orangeMat); + elbowBracket.castShadow = true; + elbow.add(elbowBracket); + + const elbowServo = new THREE.Mesh(new THREE.BoxGeometry(0.25, 0.2, 0.35), blackMat); + elbowServo.position.x = 0.05; + elbow.add(elbowServo); + + // Upper Arm + const upperArmMesh = new THREE.Mesh(new THREE.BoxGeometry(0.2, 0.4, 0.2), orangeMat); + upperArmMesh.position.y = 0.2; + upperArmMesh.castShadow = true; + elbow.add(upperArmMesh); + + // Wrist + const wrist = new THREE.Group(); + wrist.position.y = 0.4; + wrist.rotation.x = -Math.PI / 6; + elbow.add(wrist); + + const wristBracket = new THREE.Mesh(new THREE.BoxGeometry(0.35, 0.2, 0.25), orangeMat); + wristBracket.castShadow = true; + wrist.add(wristBracket); + + const wristServo = new THREE.Mesh(new THREE.BoxGeometry(0.2, 0.25, 0.2), blackMat); + wristServo.position.y = 0.1; + wrist.add(wristServo); + + // Gripper + const gripper = new THREE.Group(); + gripper.position.y = 0.1; + wrist.add(gripper); + + const gripperBase = new THREE.Mesh(new THREE.BoxGeometry(0.7, 0.1, 0.4), orangeMat); + gripperBase.position.y = 0.05; + gripperBase.castShadow = true; + gripper.add(gripperBase); + + // Fingers + const leftFinger = new THREE.Mesh(new THREE.BoxGeometry(0.1, 0.6, 0.4), orangeMat); + leftFinger.position.set(-0.3, 0.35, 0); + leftFinger.castShadow = true; + gripper.add(leftFinger); + + const rightFinger = new THREE.Mesh(new THREE.BoxGeometry(0.1, 0.6, 0.4), orangeMat); + rightFinger.position.set(0.3, 0.35, 0); + rightFinger.castShadow = true; + gripper.add(rightFinger); + + robot.add(armGroup); + + return { robot, onboardCamera, onboardRenderTarget, wheelMeshes, armGroup, lowerArm, elbow, wrist, gripper }; +} \ No newline at end of file diff --git a/frontend/src/sim/scene.ts b/frontend/src/sim/scene.ts new file mode 100644 index 00000000..4a0bbe9e --- /dev/null +++ b/frontend/src/sim/scene.ts @@ -0,0 +1,58 @@ +import * as THREE from 'three'; + +export function createScene() { + const scene = new THREE.Scene(); + scene.background = new THREE.Color(0x1a1a24); + scene.fog = new THREE.Fog(0x1a1a24, 10, 50); + return scene; +} + +export function createCamera(width: number, height: number) { + const camera = new THREE.PerspectiveCamera(60, width / height, 0.1, 1000); + camera.position.set(0, 12, 12); + camera.lookAt(0, 0, 0); + return camera; +} + +export function createRenderer(width: number, height: number) { + const renderer = new THREE.WebGLRenderer({ antialias: true, alpha: true }); + renderer.setSize(width, height); + renderer.shadowMap.enabled = true; + renderer.shadowMap.type = THREE.PCFSoftShadowMap; + return renderer; +} + +export function createLights(scene: THREE.Scene, lightPos: { x: number, y: number, z: number }) { + const ambientLight = new THREE.AmbientLight(0xffffff, 1.0); + scene.add(ambientLight); + + const dirLight = new THREE.DirectionalLight(0xffffff, 2.0); + dirLight.position.set(lightPos.x, lightPos.y, lightPos.z); + dirLight.castShadow = true; + dirLight.shadow.mapSize.width = 2048; + dirLight.shadow.mapSize.height = 2048; + dirLight.shadow.camera.near = 0.5; + dirLight.shadow.camera.far = 50; + dirLight.shadow.camera.left = -20; + dirLight.shadow.camera.right = 20; + dirLight.shadow.camera.top = 20; + dirLight.shadow.camera.bottom = -20; + scene.add(dirLight); + + return dirLight; +} + +export function createFloor(scene: THREE.Scene) { + const planeGeo = new THREE.PlaneGeometry(40, 40); + const planeMat = new THREE.MeshStandardMaterial({ + color: 0x2a2a34, + roughness: 0.8, + metalness: 0.2 + }); + const plane = new THREE.Mesh(planeGeo, planeMat); + plane.rotation.x = -Math.PI / 2; + plane.receiveShadow = true; + scene.add(plane); + + return plane; +} diff --git a/frontend/src/types.ts b/frontend/src/types.ts new file mode 100644 index 00000000..d2919dcf --- /dev/null +++ b/frontend/src/types.ts @@ -0,0 +1,84 @@ +import * as THREE from 'three'; +import * as tf from '@tensorflow/tfjs'; + +export interface RobotState { + x: number; + z: number; + rotation: number; + velocity: number; + angularVelocity: number; +} + +export type RobotConfig = string[]; +export type SceneType = 'basic' | 'living_room' | 'classroom' | 'tennis_court'; +export type SceneSize = 'small' | 'medium' | 'large'; +export type SceneComplexity = 'low' | 'medium' | 'high'; + +export interface LogEntry { + message: string; + type: 'info' | 'success' | 'warning' | 'error'; + timestamp: number; +} + +export interface Frame { + state: number[]; + image: number[][][]; + imageBase64?: string; + action: number[]; +} + +export type Episode = Frame[]; + +export interface CloudModel { + id: string; + path: string; + created_at: string; + updated_at: string; +} + +export interface CloudDataset { + id: string; + path: string; + size_bytes: number; + created_at: string; + updated_at: string; +} + +export interface CloudTrainingStatus { + status: string; + epoch: number; + num_epochs: number; + avg_loss: number | null; + progress: number; + error: string | null; + message: string | null; +} + +export interface SimulationState { + robotState: RobotState; + isRecording: boolean; + isTraining: boolean; + isInferencing: boolean; + episodes: Episode[]; + currentEpisode: Episode[]; + target: THREE.Mesh | null; + walls: THREE.Mesh[]; + environmentGroup: THREE.Group | null; + dirLight: THREE.DirectionalLight | null; + plane: THREE.Mesh | null; + robot: THREE.Group | null; + camera: THREE.PerspectiveCamera | null; + scene: THREE.Scene | null; + renderer: THREE.WebGLRenderer | null; + onboardCamera: THREE.PerspectiveCamera | null; + onboardRenderTarget: THREE.WebGLRenderTarget | null; + keys: Record; + animationFrameId: number; + inferenceTimeoutId: ReturnType; + model: tf.LayersModel | null; + recordingIntervalId: ReturnType | null; + lastX: number; + lastZ: number; + stuckCounter: number; + actionBuffer: any[]; +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json index 1ffef600..d88f175b 100644 --- a/frontend/tsconfig.json +++ b/frontend/tsconfig.json @@ -1,7 +1,26 @@ { - "files": [], - "references": [ - { "path": "./tsconfig.app.json" }, - { "path": "./tsconfig.node.json" } - ] + "compilerOptions": { + "target": "ES2022", + "experimentalDecorators": true, + "useDefineForClassFields": false, + "module": "ESNext", + "lib": [ + "ES2022", + "DOM", + "DOM.Iterable" + ], + "skipLibCheck": true, + "moduleResolution": "bundler", + "isolatedModules": true, + "moduleDetection": "force", + "allowJs": true, + "jsx": "react-jsx", + "paths": { + "@/*": [ + "./*" + ] + }, + "allowImportingTsExtensions": true, + "noEmit": true + } } diff --git a/frontend/vite.config.control.ts b/frontend/vite.config.control.ts new file mode 100644 index 00000000..c4781047 --- /dev/null +++ b/frontend/vite.config.control.ts @@ -0,0 +1,39 @@ +import tailwindcss from '@tailwindcss/vite'; +import react from '@vitejs/plugin-react'; +import path from 'path'; +import { defineConfig, loadEnv } from 'vite'; + +export default defineConfig(({ mode }) => { + const env = loadEnv(mode, '.', ''); + return { + base: '/', + plugins: [react()], + resolve: { + alias: { + '@': path.resolve(__dirname, '.'), + }, + }, + server: { + port: 5174, + host: '0.0.0.0', + proxy: { + "/api": { + target: "http://127.0.0.1:5000", + changeOrigin: true, + }, + "/socket.io": { + target: "http://127.0.0.1:5000", + ws: true, + changeOrigin: true, + }, + }, + }, + build: { + outDir: path.resolve(__dirname, '../static'), + emptyOutDir: true, + rollupOptions: { + input: path.resolve(__dirname, 'control.html'), + }, + }, + }; +}); diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts index fbdb7647..2d95d5ca 100644 --- a/frontend/vite.config.ts +++ b/frontend/vite.config.ts @@ -1,32 +1,39 @@ -import {defineConfig} from 'vite' -import react from '@vitejs/plugin-react' -import path from 'path' +import tailwindcss from '@tailwindcss/vite'; +import react from '@vitejs/plugin-react'; +import path from 'path'; +import { defineConfig, loadEnv } from 'vite'; -// https://vite.dev/config/ -export default defineConfig({ - plugins: [react()], +export default defineConfig(({ mode }) => { + const env = loadEnv(mode, '.', ''); + return { + base: '/sim3d/', + plugins: [react(), tailwindcss()], + define: { + 'process.env.GEMINI_API_KEY': JSON.stringify(env.GEMINI_API_KEY), + }, + resolve: { + alias: { + '@': path.resolve(__dirname, '.'), + }, + }, server: { - proxy: { - "/api": { - target: "http://localhost:5000", - changeOrigin: true, - }, - "/socket.io": { - target: "http://localhost:5000", - ws: false, - changeOrigin: true, - }, + port: 5173, + host: '0.0.0.0', + proxy: { + "/api": { + target: "http://127.0.0.1:5000", + changeOrigin: true, }, + "/socket.io": { + target: "http://127.0.0.1:5000", + ws: true, + changeOrigin: true, + }, + }, }, build: { - outDir: path.resolve(__dirname, '../static'), - emptyOutDir: true, - rolldownOptions: { - output: { - entryFileNames: `assets/[name].js`, - chunkFileNames: `assets/[name].js`, - assetFileNames: `assets/[name].[ext]` - } - } - } -}) + outDir: path.resolve(__dirname, '../static-3d'), + emptyOutDir: true, + }, + }; +}); diff --git a/learn1.md b/learn1.md new file mode 100644 index 00000000..e7e97480 --- /dev/null +++ b/learn1.md @@ -0,0 +1,322 @@ +## AKA-Sim (阿卡0号开源机器人) — 项目分析与学习指南 + +--- + +### 一、项目是什么 + +这是一个面向 **AKA-00 机器人小车** 的开源模拟训练平台，核心目标是实现 **ACT (Action Chunking Transformer)** 策略学习。用户可以在浏览器中操控虚拟小车、采集数据、训练神经网络、让模型自主驾驶。 + +--- + +### 二、技术栈总览 + +| 层级 | 技术 | +|------|------| +| 后端 | Python 3.11 + Flask + Flask-SocketIO | +| 机器学习 | PyTorch, einops, torchvision, accelerate | +| ACT 模型 | 自实现的 Action Chunking Transformer (~500行) | +| React 前端 | React 19 + TypeScript + Vite 8 + Zustand | +| HTML 前端 | Three.js 3D + TensorFlow.js (单文件) | +| 部署 | Docker 多阶段构建 + Docker Compose | + +--- + +### 三、项目架构 + +``` +/workspace/ +├── run.py # 主入口，启动 Flask HTTP/HTTPS +├── backend/ +│ ├── app/ +│ │ ├── __init__.py # Flask 工厂函数 +│ │ ├── extensions.py # SocketIO 实例 +│ │ └── routes/ +│ │ ├── api.py # REST API (数据集/模型/训练/推理/控制) ~640行 +│ │ ├── frontend.py # 前端路由 SPA fallback +│ │ └── websocket.py # WebSocket 事件处理 +│ ├── policies/act/ +│ │ ├── configuration_act.py # ACT 配置 +│ │ └── modeling_act.py # ACT Transformer 完整实现 ~500行 +│ ├── sim/model/car.py # 简单车体物理模型 +│ ├── train.py # 训练流程 ~390行 +│ ├── configs/types.py # 策略特征类型定义 +│ └── utils/ # 常量 & 随机工具 +├── frontend/ +│ └── src/ +│ ├── pages/ +│ │ ├── BaseControlPage.tsx # 远程控制台 +│ │ └── SimPage.tsx # 2D 模拟器 ~1050行 (核心) +│ ├── components/target/ # 目标物管理组件 +│ ├── api/socket.ts # Socket.IO + fetch 封装 +│ └── store/targetStore.ts # Zustand 状态管理 +├── main.html # 独立 Three.js 前端 ~1500行 +├── docker-compose.yml # dev/prod 两种部署 +└── Dockerfile # 多阶段构建 +``` + +--- + +### 四、推荐学习路径 + +#### 阶段 1：理解项目目标与整体架构 (1-2天) + +| 顺序 | 内容 | 文件 | +|------|------|------| +| 1 | 阅读项目文档 | `docs/src/index.md`, `docs/src/start.md` | +| 2 | 阅读入口与配置 | `run.py`, `backend/app/__init__.py` | +| 3 | 了解 API 接口 | `docs/src/api.md`, `backend/app/routes/api.py` | +| 4 | 理解通信机制 | `backend/app/routes/websocket.py`, `frontend/src/api/socket.ts` | + +**目标**: 理解前后端如何通过 REST API + WebSocket 协作。 + +#### 阶段 2：后端核心 — ACT 模型与训练 (3-5天) + +| 顺序 | 内容 | 文件 | +|------|------|------| +| 1 | 理解模型配置 | `backend/policies/act/configuration_act.py` | +| 2 | 深入 ACT 模型实现 | `backend/policies/act/modeling_act.py` | +| 3 | 理解数据集与训练流程 | `backend/train.py` | +| 4 | 理解物理模型 | `backend/sim/model/car.py` | +| 5 | 了解常量定义 | `backend/utils/constants.py` | + +**关键知识点**: +- **ACT 架构**: Transformer encoder-decoder + 可选 VAE encoder + 可选 ResNet18 视觉骨干 +- **训练流程**: 采集数据 → `.pt` 文件 → 后台线程训练 → 保存 checkpoint +- **推理流程**: 加载模型 → 5Hz 推理循环 → 输出动作向量 + +> **前置知识**: PyTorch 基础、Transformer 架构、VAE、LeRobot 数据格式 + +#### 阶段 3：前端核心 — 2D 模拟器 (3-5天) + +| 顺序 | 内容 | 文件 | +|------|------|------| +| 1 | 路由与入口 | `frontend/src/main.tsx`, `frontend/src/App.tsx` | +| 2 | 远程控制台 | `frontend/src/pages/BaseControlPage.tsx` | +| 3 | **2D 模拟器 (核心)** | `frontend/src/pages/SimPage.tsx` | +| 4 | 目标物渲染引擎 | `frontend/src/components/target/TargetRenderer.tsx` | +| 5 | 状态管理 | `frontend/src/store/targetStore.ts`, `frontend/src/model/target.ts` | + +**关键知识点**: +- Canvas 2D 渲染 (俯视图 + 伪3D第一人称射线投射) +- 20FPS 游戏循环 (requestAnimationFrame) +- 数据采集流程：开始 → 操控 → 复位保存 → 上传 +- ACT 自主推理的 5Hz 循环 + +#### 阶段 4：独立 3D 前端 (进阶，2-3天) + +| 内容 | 文件 | +|------|------| +| Three.js 3D 渲染 + 多场景 | `main.html` | +| 前端 TensorFlow.js 训练 | `main.html` 中的 ACT 模型 | +| 车载摄像头渲染 | WebGLRenderTarget | + +#### 阶段 5：部署与实战 (1-2天) + +| 内容 | 文件 | +|------|------| +| Docker 部署 | `Dockerfile`, `docker-compose.yml` | +| 真机部署 | `init.sh`, `https_init.sh` | + +--- + +### 五、动手实践建议 + +1. **先跑起来**: + ```bash + pip install -r requirements.txt + cd frontend && npm ci && npm run build && cd .. + python run.py + ``` + 打开浏览器访问，手动操控小车，体验数据采集 → 训练 → 推理的完整流程。 + +2. **阅读顺序建议**: + - `api.py` → 理解数据如何存储和查询 + - `train.py` → 理解如何从数据集训练模型 + - `modeling_act.py` → 深入理解 ACT Transformer 实现 + - `SimPage.tsx` → 理解前端如何将一切串联起来 + +3. **推荐前置知识**: + - Python Flask Web 开发 + - PyTorch 深度学习基础 + - Transformer / VAE 架构原理 + - React + TypeScript 基础 + - WebSocket 实时通信 + - Canvas 2D 绘图基础 + +--- + +### 六、核心数据流 + +``` +手动操控采集数据 → POST /api/dataset (保存 .pt) + ↓ +POST /api/train/start → 后台 PyTorch 训练 ACT + ↓ +POST /api/infer/start → POST /api/infer/step (5Hz) + ↓ +模型输出动作 → 小车自动行驶 +``` + +总结：这个项目是一个非常好的 **端到端机器人学习** 实战项目，涵盖了数据采集、模型训练、在线推理、Web 可视化等完整链路，适合对具身智能/机器人学习方向感兴趣的开发者深入学习。 + + + +## 前后端 REST API + WebSocket 协作分析 + +### 项目概览 + +AKA-Sim 是一个面向 AKA-00 机器人小车的模拟训练平台，实现了 ACT (Action Chunking Transformer) 策略学习。核心数据流是：**手动控制采集数据 → 训练 ACT 模型 → 模型自主推理**。 + +--- + +### 架构总览 + +``` +┌──────────────────────────────────────────────────┐ +│ 单一 Flask 容器 │ +│ │ +│ React SPA (/:80) ──┐ │ +│ Three.js (/:80) ──┤ │ +│ ▼ │ +│ ┌─── REST API (/api/*) ────┐ │ +│ │ 数据集 CRUD │ │ +│ │ 训练 启动/状态查询 │ ←─ fetch 轮询 │ +│ │ 推理 启动/步骤/停止 │ ←─ 5Hz POST │ +│ │ 远程控制代理 │ │ +│ └──────────────────────────┘ │ +│ │ +│ ┌─── Socket.IO (/socket.io) ─┐ │ +│ │ action C → S │ ←─ 20FPS 实时 │ +│ │ get_car_state C → S │ │ +│ │ reset_car_state C → S │ │ +│ │ car_state S → C │ ←─ 状态广播 │ +│ │ act_infer C → S │ ←─ WS推理(备用) │ +│ │ act_action S → C │ │ +│ └──────────────────────────┘ │ +│ │ +│ PyTorch ACT Model (线程内) │ +│ CarModel 单例 (全局状态) │ +└──────────────────────────────────────────────────┘ +``` + +--- + +### 通信职责划分 + +**REST API** — 用于**数据密集型、非实时**操作： + +| 端点 | 方法 | 用途 | 调用频率 | +|------|------|------|---------| +| `/api/dataset` | POST | 保存采集数据集 | 采集结束时 1 次 | +| `/api/datasets` | GET | 列出数据集 | 初始化 + 采集后刷新 | +| `/api/datasets/` | DELETE | 删除数据集 | 手动 | +| `/api/models` | GET | 列出模型 | 初始化 + 手动 | +| `/api/models/` | DELETE | 删除模型 | 手动 | +| `/api/train/start` | POST | 启动训练 | 按钮触发 | +| `/api/train/status` | GET | 查询训练进度 | **1 秒轮询** | +| `/api/infer/start` | POST | 加载推理模型 | 按钮触发 | +| `/api/infer/step` | POST | 执行一步推理 | **5Hz 轮询** | +| `/api/infer/stop` | POST | 停止推理 | 按钮触发 | +| `/api/control` | GET | 远程控制代理 | 实车模式 | +| `/api/ip` | GET | 获取本机 IP | 初始化 | + +**Socket.IO** — 用于**高频实时**操作： + +| 事件 | 方向 | 用途 | 频率 | +|------|------|------|------| +| `action` | C→S | 发送控制指令 (up/down/left/right/stop) | **20FPS** | +| `car_state` | S→C | 广播小车位置/角度 | 每次 action 后 | +| `get_car_state` | C→S | 查询当前状态 | 初始化时 1 次 | +| `reset_car_state` | C→S | 重置小车 | 复位按钮 | +| `act_infer` / `act_action` | C↔S | ACT 推理 (备用通道) | 按需 | + +> **注意**: Socket.IO 配置为 `transports: ["polling"]`, `upgrade: false`，实际使用 HTTP 长轮询而非原生 WebSocket。 + +--- + +### 核心业务流程 + +#### 1. 数据采集 (手动控制) + +``` +用户 WASD 键盘 + → socket.emit('action', 'up') [20FPS] + → 后端 handle_action() 更新 CarModel + → socket.emit('car_state', {x,y,angle}) + → 前端更新画布坐标 + +同时前端本地采集: + → getObservationVectors() [state[14], envState[7]] + → fpvCanvas.toDataURL() [base64 截图] + → push 到 currentEpisodeRef +``` + +#### 2. 数据集保存 + +``` +采集结束 → packDataset(episodes) + → POST /api/dataset {states, env_states, actions, action_is_pad, images} + → 后端 base64 解码图片 → torch.save() → output/datasets/*.pt +``` + +#### 3. 模型训练 + +``` +POST /api/train/start {dataset_path, num_epochs, ...} + → 后端 threading.Thread 后台运行 train_act() + → 更新全局 train_state + ← GET /api/train/status (前端 1s 轮询) + → 返回 {status, epoch, avg_loss, progress} +``` + +#### 4. ACT 自主推理 + +``` +POST /api/infer/start {model_id} → 加载模型 +POST /api/infer/step {state, env_state} → 5Hz 推理循环 + → 后端 ACT 前向推理 → _map_action() → "up" + → _apply_action() → emit('car_state') + ← 返回 {action: "up", action_vector: [...]} + → 前端 actCommandRef 控制 updatePhysics() +``` + +--- + +### 关键架构特点 + +1. **混合通信策略**: REST 处理数据 CRUD 和训练流程，Socket.IO 处理高频实时控制，职责清晰 +2. **推理双通道**: SimPage 的 ACT 推理走 REST `POST /api/infer/step`（带状态同步），WebSocket 的 `act_infer` 是备用通道（Three.js 前端使用） +3. **无认证无数据库**: 所有端点完全开放，数据以 `.pt` 文件存储，零外部依赖 +4. **坐标偏移**: 后端 CarModel 以 `(0,0)` 为中心，前端画布偏移 `(400,300)` 映射 +5. **SPA 部署**: 生产环境 Flask 同时提供 API + React 静态文件，开发环境 Vite 代理 `/api` 和 `/socket.io` 到 Flask 5000 端口 + + + + +## `main.html` vs React 前端对比分析 + +`main.html` 是一个**独立的 3D 模拟器页面**，与当前 React 前端的 `/sim` 页面功能相似但实现完全不同： + +### 核心差异 + +| 维度 | `main.html` (Three.js) | React 前端 `/sim` (Canvas 2D) | +|------|----------------------|------------------------------| +| **3D 渲染** | **Three.js + WebGL**，真3D场景（网格体、材质、光影） | **Canvas 2D 光线投射**（Wolfenstein 3D 风格伪3D） | +| **视觉效果** | 纹理地面（木纹/瓷砖/草地）、动态光照、阴影、雾效 | 扁平色块，无纹理、无光影，像素风 | +| **场景** | 4种场景（基础/客厅/教室/网球场）+ 复杂度调节 + 光源控制 | 固定网格，自由放置矩形/圆形障碍物 | +| **相机** | 第三人称跟随相机 + 车载第一人称相机（320x240） | 俯视图 + 伪3D第一人称 | +| **物理引擎** | 简单碰撞检测，无旋转碰撞 | 支持旋转矩形碰撞、圆形碰撞 | +| **前端训练** | TensorFlow.js 在浏览器端训练 ACT 模型 | 无前端训练，仅云端训练 | +| **训练模式** | 双模式：**前端TF.js训练** + **云端训练** | 仅云端训练 | +| **推理** | 前端 TF.js 推理（Temporal Ensembling）+ 云端推理 | 仅云端推理 5Hz | +| **目标管理** | 固定目标点（红色方块 + 黄色球），无编辑器 | 完整 CRUD：创建/编辑/删除/拖拽/导入导出 | +| **数据采集** | 64x64 图像 + 14维状态向量 + one-hot action | 类似，320x240 图像作为 base64 | +| **通信** | 仅 REST API（无 WebSocket） | Socket.IO + REST API | +| **状态持久化** | 无 | Zustand + localStorage | + +### 关键总结 + +1. **`main.html` 更"炫"** — Three.js 真正的 3D 渲染，多场景多纹理，视觉效果远超 React 版 +2. **`main.html` 更完整的功能闭环** — 支持浏览器端 TF.js 训练 + 推理，不依赖后端也能完成 AI 流程 +3. **React 前端更实用** — 目标编辑器更完善（拖拽/导入导出/持久化），WebSocket 实时通信，更适合实际数据采集 +4. **两者互不替代** — `main.html` 像是一个早期原型或演示页面，React 前端是正式版本 \ No newline at end of file diff --git a/learn2.md b/learn2.md new file mode 100644 index 00000000..c0e512f4 --- /dev/null +++ b/learn2.md @@ -0,0 +1,194 @@ +## 前后端数据交互分析 + +### 一、整体通信架构 + +前端与后端通过**两种通道**交互： + +| 通道 | 技术 | 用途 | +|------|------|------| +| **HTTP REST** | `fetch` → Flask Blueprint `/api/*` | 数据集保存/列表/删除、训练启动/状态、推理启动/步进/停止、模型管理 | +| **WebSocket** | Socket.IO (polling) → Flask-SocketIO | 实时小车动作下发、状态回传、ACT推理 | + +--- + +### 二、信息采集流程 + +采集分为 **前端本地采集** 和 **后端共享采集** 两条并行路径： + +#### 1. 前端本地模拟 + 采集（`SimPage.tsx`） + +这是核心采集路径，**所有模拟和采集都在前端完成，不需要后端实时参与**。 + +**渲染循环**（20 FPS，`692:763`）： + +``` +requestAnimationFrame → 50ms 节流 + ├─ ACT推理（5Hz，每200ms一次 POST /api/infer/step） + ├─ updatePhysics()（物理更新：键盘→指令→移动→碰撞检测） + ├─ drawTopDown()（俯视图Canvas渲染） + ├─ drawFirstPerson()（伪3D第一人称Canvas渲染） + └─ 数据采集（如果 collecting=true） +``` + +**每帧采集的数据**（`743:762`）： + +```typescript +EpisodeStep = { + state: number[]; // 14维 — 小车自身状态 + envState: number[]; // 7维 — 环境观测状态 + action: number[]; // 5维 — one-hot 动作编码 + image?: string; // base64 PNG — 第一人称画布截图 +} +``` + +**`getObservationVectors()`**（`496:515`）构建观测向量： + +- **state（14维）**：`[x, y, angle, speed, ballDistance, 0,0,0,0,0,0,0,0,0]` — 只用了前5维 +- **envState（7维）**：`[x, y, angle, speed, isCollision, forwardDistance, ballDistance]` + +其中 `forwardDistance` 通过射线-线段求交（`465:479`）计算前方障碍物距离，`ballDistance` 计算到最近圆形目标的距离。 + +**动作编码**（`450:463`）： + +| 指令 | one-hot向量 | +|------|------------| +| up | `[1,0,0,0,0]` | +| down | `[0,1,0,0,0]` | +| left | `[0,0,1,0,0]` | +| right | `[0,0,0,1,0]` | +| stop | `[0,0,0,0,5]` | + +**采样策略**（`746:751`）： +- 非stop指令：每帧都记录 +- stop指令：每5帧记录1次（`STOP_RECORD_STRIDE=5`），避免冗余数据 + +**回合管理**（`828:858`）： +- 每回合至少15步（`MIN_STEPS_PER_EPISODE=15`） +- 用户点击"复位保存" → 当前回合存入 `episodesRef` → 小车重置到地图中心 +- 达到目标回合数（默认8轮）后自动结束采集 + +#### 2. 前端 → 后端的数据保存 + +`packDataset()`（`517:556`）将所有回合数据打包为 chunk 结构： + +``` +每10步为一个chunk（CHUNK_SIZE=10） + ├─ state: 取chunk第一步的state（1×14） + ├─ env_state: 取chunk第一步的envState（1×7） + ├─ action: chunk内所有步的动作序列（10×5） + ├─ action_is_pad: padding标记（不足10步补0，padding位置为1） + └─ images: 每步的第一人称截图 base64（10×1） +``` + +通过 `POST /api/dataset` 发送到后端，后端（`api.py:65:185`）： +1. 验证 JSON payload 完整性 +2. 将 states/env_states/actions/action_is_pad 转为 `torch.Tensor` +3. 解码 base64 图片 → 保存为 PNG 文件到 `output/datasets/images_xxx/` 目录 +4. 记录图片相对路径到 dataset 的 `obs_images` 字段 +5. 整体序列化为 `act_dataset_xxx.pt`（PyTorch 文件） + +--- + +### 三、训练流程 + +#### 1. 触发训练 + +前端 `startTrain()`（`239:260`）→ `POST /api/train/start` → 后端启动**独立线程** + +后端 `_run_training()`（`287:336`）： +``` +train_thread (daemon=True) + └─ train_from_dataset() + ├─ load_dataset_from_local(path) → torch.load → ACTDataset + ├─ build_config(state_dim=14, env_state_dim=7, action_dim=5, chunk_size=10) + ├─ ACT(config) → 创建 Transformer 模型 + └─ train_act() → 训练循环 +``` + +#### 2. 训练细节（`train.py:44:174`） + +**模型架构**（ACT - Action Chunking Transformer）： +- 输入：state（14维）+ env_state（7维）→ 线性投影 → Transformer Encoder +- 输出：Transformer Decoder → 生成 **chunk_size 步** 的动作序列（10×5） +- 可选 VAE：encoder 将动作编码为 latent → decoder 重建（本场景默认关闭） + +**训练循环**： +``` +每 epoch: + 每 batch: + 1. model(batch) → actions_pred（B,10,5）, (mu, log_sigma) + 2. loss = MSE(actions_pred, actions_gt) // 行为克隆 + 3. 可选: loss += kl_weight * KL_loss // VAE正则 + 4. loss.backward() → grad_clip(1.0) → optimizer.step() + 5. progress_callback(epoch, total, avg_loss) → 更新 train_state（线程安全） +``` + +**前端轮询训练状态**（`222:237`）：每秒 `GET /api/train/status` 获取 epoch、loss、progress + +#### 3. 训练产物 + +保存到 `output/train/act_YYYYMMDD_HHMMSS/`： +- `act_checkpoint.pt` — 包含 `model_state_dict` + `state_dim` + `env_state_dim` + `action_dim` + `chunk_size` +- `training_metrics.json` — 每步的 loss、lr、epoch 等指标 + +--- + +### 四、推理流程 + +#### 1. 加载模型 + +前端选择模型 → `POST /api/infer/start` → 后端（`api.py:490:528`）： +1. `torch.load(checkpoint)` → 读取维度参数 +2. `build_config()` + `ACT(config)` + `load_state_dict()` → 恢复模型 +3. 存入 `infer_state`（内存中常驻） + +#### 2. 步进推理 + +渲染循环中 5Hz 触发（`701:738`）： +``` +每 200ms: + 1. getObservationVectors() → 获取当前 {state:14维, env_state:7维} + 2. POST /api/infer/step {state, env_state} + 3. 后端: tensor(state) → model({obs_state, env_state}) → actions[0,0] → _map_action() + 4. _map_action(): 取5维向量中最大值索引 → "up"/"down"/"left"/"right"/"stop" + 5. _apply_action(): 更新后端 car 物理状态 + socketio.emit('car_state') + 6. 返回 {status:"ok", action:"up"} → 前端 actCommandRef.current = "up" + 7. updatePhysics() 读取 actCommandRef → 驱动前端小车移动 +``` + +关键点：推理时**后端也同步驱动了 car 单例**（`_apply_action`），并通过 WebSocket 广播状态，但前端主要用返回的 action 字符串在本地 `applyLocalAction()` 驱动渲染。 + +--- + +### 五、数据流全景图 + +``` +┌──────────────── 前端（SimPage.tsx）────────────────┐ +│ │ +│ 键盘WASD ──→ 指令 ──→ applyLocalAction() │ +│ │ ├─ 物理更新(本地carState) │ +│ │ ├─ 碰撞检测 │ +│ │ └─ emit('action') ──────────→│──→ Socket.IO ──→ 后端 car 单例 +│ │ │ +│ 采集时: │ +│ 每帧 getObservationVectors() → {state, envState} │ +│ commandToActionVec(cmd) → [one-hot] │ +│ fpvCanvas.toDataURL() → base64 PNG │ +│ → EpisodeStep 存入 currentEpisode │ +│ │ +│ 复位保存 → packDataset() → POST /api/dataset ──────→│──→ HTTP ──→ 后端保存 .pt + PNG +│ │ +│ 开始训练 → POST /api/train/start ─────────────────→│──→ HTTP ──→ 后端训练线程 +│ 轮询状态 → GET /api/train/status ←─────────────────│←── HTTP ←── train_state +│ │ +│ ACT推理(5Hz): │ +│ state+envState → POST /api/infer/step ────────────→│──→ HTTP ──→ 后端 model() +│ ←── {action:"up"} ←───────────────────────────────│←── HTTP ←── action映射 +│ actCommandRef = "up" → applyLocalAction("up") │ +│ │ +│ 俯视图 Canvas 第一人称 Canvas │ +│ (800×600 2D) (320×240 伪3D) │ +└─────────────────────────────────────────────────────┘ +``` + +**核心设计思想**：前端完全自治模拟物理和渲染，后端只负责三件事 —— 数据持久化（`.pt`）、模型训练（ACT Transformer）、模型推理（tensor forward pass）。前端通过 HTTP API 与后端交互，两者是松耦合的。 \ No newline at end of file diff --git a/main.html b/main.html index 5292c9bd..bfc28659 100644 --- a/main.html +++ b/main.html @@ -3,7 +3,7 @@ - LeRobot ACT Simulator (Main) + AKA ACT Simulator (Main) @@ -47,7 +47,7 @@
🤖
-

LeRobot ACT Simulator

+

AKA ACT Simulator

Action Chunking with Transformers - Main Edition

@@ -100,8 +100,11 @@

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场景设置

-
+ +