自然勾配による共有制御
Natural Gradient Shared Control
ユーザー操作と自律制御を融合する共有制御の新手法を提案し、ロボット方策のフィッシャー情報を近似して自然勾配でユーザー操作を補助する。操作タスクのユーザー研究で、制御権を保ちつつ効率的にタスクを完了できることを示した。
著者: Yoojin Oh, Shao-Wen Wu, Marc Toussaint, Jim Mainprice
分類: cs.RO
原文アブストラクト
We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear. Our proposed solution relies on natural gradients emerging from the divergence constraint between the robot and the shared policy. We approximate the Fisher information by sampling a learned robot policy and computing the local gradient to augment the user control when necessary. A user study performed on a manipulation task demonstrates that our approach allows for more efficient task completion while keeping control authority against a number of baseline methods.
