学習されたゼロダイナミクスポリシーによる堅牢な機敏性
Robust Agility via Learned Zero Dynamics Policies
劣駆動ハイブリッド系に対し、最適制御の不変写像を学習して次元削減しつつ安定性を保つゼロダイナミクスポリシーを提案し、3DホッピングロボットARCHERで不整地外乱下の安定跳躍を実証した。
著者: Noel Csomay-Shanklin, William D. Compton, Ivan Dario Jimenez Rodriguez, Eric R. Ambrose, Yisong Yue, Aaron D. Ames
分類: cs.RO
原文アブストラクト
We study the design of robust and agile controllers for hybrid underactuated systems. Our approach breaks down the task of creating a stabilizing controller into: 1) learning a mapping that is invariant under optimal control, and 2) driving the actuated coordinates to the output of that mapping. This approach, termed Zero Dynamics Policies, exploits the structure of underactuation by restricting the inputs of the target mapping to the subset of degrees of freedom that cannot be directly actuated, thereby achieving significant dimension reduction. Furthermore, we retain the stability and constraint satisfaction of optimal control while reducing the online computational overhead. We prove that controllers of this type stabilize hybrid underactuated systems and experimentally validate our approach on the 3D hopping platform, ARCHER. Over the course of 3000 hops the proposed framework demonstrates robust agility, maintaining stable hopping while rejecting disturbances on rough terrain.
