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Copy file name to clipboardExpand all lines: docs/source/experimental-features/bleeding-edge.rst
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@@ -9,3 +9,144 @@ Directly integrating such features before they are complete and without feedback
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To address this, some major features will be released as Experimental Feature Branches.
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This way, the community can experiment with and contribute to the feature before it's fully integrated, reducing the likelihood of being derailed by unexpected and new errors.
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RL Post-Training for VLA Models
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---------------------------------
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`RLinf <https://github.com/RLinf/RLinf.git>`_ is a flexible and scalable open-source RL infrastructure designed for
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Embodied and Agentic AI. This integration enables **reinforcement learning fine-tuning of Vision-Language-Action
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(VLA) models** (e.g., GR00T, OpenVLA) on Isaac Lab simulation tasks.
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The typical workflow follows three stages:
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1. **Data collection** — Collect demonstration data from the Isaac Lab environment (e.g., via teleoperation or scripted policy).
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2. **Base model training** — Train a VLA base model (e.g., GR00T) on the collected demonstrations using supervised learning.
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3. **RL fine-tuning** — Fine-tune the pretrained VLA model on the Isaac Lab task using RLinf with PPO / Actor-Critic / SAC.
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Overview
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~~~~~~~~
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The RLinf integration allows Isaac Lab users to:
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- Fine-tune pretrained VLA models on Isaac Lab tasks using PPO / Actor-Critic / SAC
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- Leverage RLinf's FSDP-based distributed training across multiple GPUs/nodes
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- Define observation/action mappings from Isaac Lab to GR00T format via a single YAML config
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- Register Isaac Lab tasks into RLinf without modifying RLinf source code
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