サブポリシー間の不一致を利用した人間とロボットの制御調停の学習
Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies
ロボットが複数の選択肢に直面する決定点をサブポリシーの混合分布のモダリティから検出し、強化学習で人間とロボットの制御権限を適切に配分する調停戦略を学習した。遠隔操作実験で直接操作より性能が向上した。
著者: Yoojin Oh, Marc Toussaint, Jim Mainprice
分類: cs.RO
原文アブストラクト
In the context of teleoperation, arbitration refers to deciding how to blend between human and autonomous robot commands. We present a reinforcement learning solution that learns an optimal arbitration strategy that allocates more control authority to the human when the robot comes across a decision point in the task. A decision point is where the robot encounters multiple options (sub-policies), such as having multiple paths to get around an obstacle or deciding between two candidate goals. By expressing each directional sub-policy as a von Mises distribution, we identify the decision points by observing the modality of the mixture distribution. Our reward function reasons on this modality and prioritizes to match its learned policy to either the user or the robot accordingly. We report teleoperation experiments on reach-and-grasping objects using a robot manipulator arm with different simulated human controllers. Results indicate that our shared control agent outperforms direct control and improves the teleoperation performance among different users. Using our reward term enables flexible blending between human and robot commands while maintaining safe and accurate teleoperation.
