実行失敗を継承可能な能力へ変える複合的物理RSI
Compounding Physical RSI: Turning Execution Failure into Inherited Capability
ロボット操作ポリシーが失敗から段階的に自己改善する枠組みを提案し、失敗箇所の特定・修正経験の選別・既存技能の保持により世代を重ねて能力を複利的に向上させる。
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: Yixuan Yang, Qiang Chen, Run Shao, Shuwei Dong, Wenhao Li, Yunpeng Wang, Wenhan Fang, Haomiao Wu, Xingyu Wang, Lichen Man, Rong Zhang, Xuezhi Cui, Yuan Liu, Bingyan Lu, Jiawei Ma, Xiaoyang Ma, Zhemin Sun, Zhuorui Yao, Yutong Dong, Yuxuan Zhao, Yichong Mu, Jiabao Jiang, Wenxuan Deng, Junling Zhou, Yi Chen, Deyu Zhang, Yiming Yan, Yige Yuan, Shan You, Fei Wang, Haifeng Li, Hongyan Xu, Yichao Cao, Xiu Su
分類: cs.RO
原文アブストラクト
Generalist robot policies are trained on static offline datasets, yet physical manipulation unfolds sequentially across stages. Because a weakness in a downstream stage remains unobservable until the policy passes earlier ones, each resolved bottleneck exposes the next hidden failure mode. This staged structure calls for Recursive Self-Improvement (RSI), in which a policy iteratively executes, diagnoses failures, and updates itself. Moreover, because task completion behaves like a product of stage-level pass rates, recursive gains can multiply across generations. In physical environments, three bottlenecks obstruct compounding: terminal execution feedback rarely localizes the stage that caused a failure, failed trajectories do not supply the corrective action, and updating shared parameters can degrade previously mastered skills. We propose Compounding Physical RSI, a self-evolving policy framework that turns execution failures into heritable capability. First, automated capability-gap assessment traces failed rollouts to the task stage where progression breaks down and formulates a structured improvement objective. Second, conditioned on this objective, our remedy stage curates validated, stage-relevant experience and refines the policy via behavior-regularized offline reinforcement learning, without requiring costly online rollouts at each optimization step. Third, to safeguard existing skills, our retention stage constructs weight-space candidates between the inherited reference policy and the refined policy, evaluates them across the task suite, and promotes the best candidate as the reference state for the next generation. Evaluated across all 42 tasks of the RoboDojo-Sim benchmark, CSU-AI-07 ranks first on the leaderboard with a three-seed mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.