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ALOE — Project Page

Project page for ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training. The paper is available on arXiv; you can link to this page from the paper.

Local Preview

From the repo root, run a static server:

# Python 3
python -m http.server 8000

# or
npx serve .

Then open http://localhost:8000 in your browser.

Page Structure

Single-page layout with no pagination:

  • Abstract — Paper abstract
  • Overview Figure — Teaser/process figure under the abstract
  • Method — Mixed replay intuition, behavior-policy value mismatch, ALOE's action-value definition, Q-chunking, pessimistic ensemble, and advantage-weighted policy extraction
  • Task Demos — Four task videos (phone, cloth, pick-and-place, phone assembly)
  • Continuous Operation — Phone packing 21-trial video
  • Final Results — four-task success-rate and policy-improvement figures
  • Robustness — Phone disturb ×3 + cloth disturb ×1
  • OOD Generalization — Unseen object pick videos
  • Critic Analysis — Critic ablations, calibration, action ranking, and component analysis
  • Contributions — Bullet points
  • Citation — BibTeX with the arXiv ID

What to Edit

Where What
Action links (top) Set Paper (PDF) to your arXiv URL; Code and Video to repo/demo links
Authors Update href to personal/Google Scholar pages if desired
BibTeX Keep authors and arXiv metadata aligned with the latest paper source
images/ Keep generated paper figures such as final_result.pdf, policy_improvement.pdf, and throughput_generalization.pdf in sync

Videos in videos/ and images in images/ are used as-is; paths are in index.html.

GitHub Pages

  1. Push this repo to GitHub (e.g. Rooshy-yang/aloe).
  2. Go to Settings → Pages.
  3. Source: Deploy from a branch.
  4. Branch: main, Folder: / (root).
  5. Save. After a minute or two, the site will be at:

Link in Your Paper

In the paper, point to the project page:

Project page: https://rooshy-yang.github.io/aloe/

Repo Layout

├── index.html      # Main page
├── style.css       # Styles
├── README.md       # This file
├── images/         # teaser.png, Q_value2_cropped.png, final result figures
└── videos/         # Task demos, robustness, OOD, phone_success_rate, etc.

After editing, commit and push to update the live page.

About

Home page for ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training https://arxiv.org/abs/2602.12691

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