状態行動を超えて：ロボット模倣学習における指令と状態の不一致の活用
Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning
ロボットの模倣学習において、指令と実際の状態の不一致を重み付けに利用する手法CSDWを提案し、制約のある実機タスクで性能を向上させた。
著者: Peiyan Li, Yueran Tao, Enhao Zhang, Zhixuan Zhao, Chenghao Yue, Hao Wang, Lei Lv, Wentao Zhao, Jiahao Chen, Xin Liu, Kangyao Huang, Yu Luo, Huaping Liu
分類: cs.RO
原文アブストラクト
Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may reflect control demands that motion alone does not capture. We investigate when this information matters and how to exploit it. Across three real-robot tasks, task and phase analyses reveal larger supervision gaps under constrained interaction, while selective command retention provides evidence of locally useful command information. Building on these findings, we propose Command-State Discrepancy Weighting (CSDW), which accounts for robot response times and combines subsequent progress, persistent unmet demand, and demand changes into continuous weights for command supervision. The method requires no task-phase annotations or changes to policy architecture or inference. CSDW improves over uniform command supervision on constrained tasks, while methods perform similarly in the less constrained task.