Embodiment-Classifier Guidance: Transferring Frozen VLAs Across Robot Dynamics without Demonstrations
Flow-based policies such as $π_0$ are trained on a fixed set of embodiments, and transferring them to new ones is an open problem. To adapt a frozen $π_0$ to a new embodiment without additional demonstrations or parameter updates, we train a lightweight embodiment classifier to distinguish successful action chunks on the source body from those on the target body, using a few calibration tasks. On transfer tasks that $π_0$ saw on its source body but the classifier was never trained on, we nudge each denoising step of $π_0$ with the classifier's gradient, which estimates the direction from the distribution of successful actions on the source body toward that on the target body. On the LIBER...
| Authors | Devin Han, Hershraj Niranjani |
|---|---|
| Primary category | cs.RO |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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