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Latent Policy Steering: An Efficient and Flexible Framework for Cross-Embodiment Transfer

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

The performance of learned robot visuomotor policies depends heavily on the size and quality of their training data, yet collecting high-quality demonstrations remains costly for robots in the real world. Although large-scale robot and human datasets are increasingly available, embodiment gaps and mismatched action spaces make them difficult to leverage directly. Cross-embodiment transfer, reusing experience from other embodiments to improve learning on a target embodiment, is therefore crucial for scaling robot learning beyond per-robot data collection. In this work, we find that efficient tr

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