An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning
Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. However, policies today remain brittle to viewpoint perturbations, making deployment in diverse environments a challenge. We present a controlled empirical study of which design choices allow visuomotor policies to generalize across viewpoints. We find that viewpoint generalization improves when dense visual tokens are retained and the action head participates in geometric reasoning. On a suite of simulated tasks that span a wide range of camera poses, we show that these design choices yield a policy that remains performant across viewpoints. As a practi...
| Authors | Mino Nakura, Sriram Krishna, Yufei Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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