ピクセルからポーズへ：人間のデモンストレーションによる物体中心のツール操作学習
From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
人間の動画デモから物体中心の世界モデルを事前学習し、ポーズ認識方策と組み合わせることで、ロボットとの対応データなしに複雑なツール操作を実現した。
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: Bangjun Wang, Longyan Wu, Yukun Wei, Shenghe Shao, Chaoyi Huang, Wenze Cui, Zetong Xu, Hanlin Wu, Long Chen, Yi Ma, Hongyang Li
分類: cs.RO, cs.AI
原文アブストラクト
Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approaches leverage human video demonstrations to mitigate this shortage, they remain computationally expensive and still rely on paired human-robot data for domain alignment. Although current state-of-the-arts excel at long-horizon tasks, they struggle with the delicate and precise control required for complex tool manipulation. To overcome these limitations, we introduce P2P-T, from Pixel to Poses for Tool Manipulation, a data-efficient, object-centric framework that learns tool use directly from human demonstrations. P2P-T bridges the cognitive and physical execution gap through a two-stage approach. First, pretraining an object-centric world model to extract stable pose priors; second, integrating these priors into an efficient, pose-aware low-level policy. By utilizing a robust automated data processing pipeline powered by modern foundation models, P2P-T completely bypasses the need for human-robot aligned data. This reduces overall training overhead drastically. With minimal per-task fine-tuning, our framework achieves a 73% improvement over the previous state of the art in execution performance on complex, real-world tool manipulation tasks that currently remain out of reach for standard large-scale pretrained models.