生成的アクションチャンクサンプリングによる物理的ヒューマンロボット協調における適応剛性制御
Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration
RGB画像と外部トルク推定に基づき、生成ポリシーが複数の未来アクションチャンクをサンプリングし、そのばらつきに応じて関節剛性と減衰を適応させる手法を提案。協調搬送タスクで成功率0.95を達成し、ばらつきが剛性調整のオンライン信号として有効であることを示した。
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著者: Aoi Otake, Ferdinand Hartmann, Ko Igari, Shingo Murata
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Physical human-robot collaboration requires a robot to provide assistance when human intention is clear while remaining compliant when several future motions are plausible. We present an adaptive stiffness framework based on generative action-chunk sampling. Conditioned on an RGB image and external joint-torque estimates, the policy samples multiple future action chunks from an observation-conditioned prior. Variation among the sampled action chunks is used to continuously adapt joint stiffness and damping. Greater variation makes the robot more compliant to facilitate human guidance, whereas lower variation provides firmer assistance. In a real-world collaborative transport task with four possible directions, the proposed method achieved an average success rate of 0.95, compared with 0.83 for a fixed-stiffness ablation and 0.69 for a deterministic baseline. Near direction determination, variation among the sampled action chunks increased and the controller accordingly reduced stiffness. These results suggest that variation among actions sampled by a generative policy can serve as an online control signal for balancing assistance and compliance in physical human-robot interaction.