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arXiv Machine Learning By Devin Han, Hershraj Niranjani

Embodiment-Classifier Guidance: Transferring Frozen VLAs Across Robot Dynamics without Demonstrations

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The paper introduces Embodiment‑Classifier Guidance, a method that adapts a frozen flow‑based policy π_0 to new robot embodiments without additional demonstrations or parameter updates. By training a lightweight classifier to distinguish successful action chunks between source and target bodies, the policy’s denoising steps are nudged toward the target distribution using the classifier’s gradient. Experiments on the LIBERO simulation benchmark show that this guidance improves average success rates from 72% to 80% across 12 controller–body pairs, reduces failures by 31%, and demonstrates data‑efficient adaptation with only a few target‑body rollouts.

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