視点一般化可能な視覚模倣学習ポリシーに何が重要か：実証研究
An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning
視覚模倣学習において、密な視覚トークンの保持と幾何学的推論を行う行動ヘッドが視点一般化に有効であることを実験的に示し、シミュレーションから実世界へのゼロショット転移も実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Mino Nakura, Sriram Krishna, Yufei Wang, Shubham Tulsiani, Zackory Erickson, David Held
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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 practical consequence, a policy trained with these design choices also transfers zero-shot from simulation to the real world under random camera configurations.
