単調な進捗を超えて：ロボット模倣のためのリトライ監視価値学習
Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
不完全なデモからリトライイベントを疎な監視として用い、誤りに敏感な価値関数を学習し、下流の行動クローニングの重み付けに活用するフレームワークを提案。実ロボット操作タスクで効果を実証。
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: Xinyao Qin, Junjie Lu, Kaixin Wang, Chuheng Zhang, Sinjae Kang, Kimin Lee, Min Xu, Bin Liang, Jun Yang, Li Zhao
分類: cs.RO
原文アブストラクト
Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstable contact, and repeated attempts. While these segments are commonly treated as noisy or suboptimal data, they provide valuable evidence about when execution deviates from a desirable path and how task feasibility can be restored. However, existing reward and value models often rely on monotonic progress assumptions, which capture coarse task advancement but may overlook local execution errors and corrective behaviors in imperfect demonstrations. In this work, we propose ReTVL (ReTry-Supervised Value Learning), a framework for learning mistake-sensitive value functions from mixed-quality robot demonstrations by leveraging retry events as sparse supervision. ReTVL captures the local degradation-and-recovery structure around mistakes by combining global progress calibration with local pairwise preference learning induced by sparsely annotated retry keypoints. The learned value model is then used to reweight demonstration chunks for downstream behavior cloning, reducing the influence of harmful execution errors while preserving useful corrective behaviors. Experiments on real-robot manipulation tasks show that ReTVL produces more fine-grained value estimates than progress-based baselines and improves imitation learning from imperfect demonstrations.
