MimicDroid: 人間の遊び動画からの文脈内学習によるヒューマノイドロボット操作
MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos
人間の遊び動画のみを訓練データとして、ヒューマノイドが新しい操作タスクを数例の動画から効率的に学習できる文脈内学習手法を提案し、実世界で成功率を約2倍に向上させた。
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？
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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？
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著者: Rutav Shah, Shuijing Liu, Qi Wang, Zhenyu Jiang, Sateesh Kumar, Mingyo Seo, Roberto Martín-Martín, Yuke Zhu
分類: cs.RO
原文アブストラクト
We aim to enable humanoid robots to efficiently solve new manipulation tasks from a few video examples. In-context learning (ICL) is a promising framework for achieving this goal due to its test-time data efficiency and rapid adaptability. However, current ICL methods rely on labor-intensive teleoperated data for training, which restricts scalability. We propose using human play videos -- continuous, unlabeled videos of people interacting freely with their environment -- as a scalable and diverse training data source. We introduce MimicDroid, which enables humanoids to perform ICL using human play videos as the only training data. MimicDroid extracts trajectory pairs with similar manipulation behaviors and trains the policy to predict the actions of one trajectory conditioned on the other. Through this process, the model acquired ICL capabilities for adapting to novel objects and environments at test time. To bridge the embodiment gap, MimicDroid first retargets human wrist poses estimated from RGB videos to the humanoid, leveraging kinematic similarity. It also applies random patch masking during training to reduce overfitting to human-specific cues and improve robustness to visual differences. To evaluate few-shot learning for humanoids, we introduce an open-source simulation benchmark with increasing levels of generalization difficulty. MimicDroid outperformed state-of-the-art methods and achieved nearly twofold higher success rates in the real world. Additional materials can be found on: ut-austin-rpl.github.io/MimicDroid
