歩き方を学ぶ：動作記憶による最適制御ソルバのウォームスタート
Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion
ヒューマノイドの歩行計画のため、オフライン生成した全身軌道を動作記憶として学習し、最適制御ソルバの初期解に用いることで収束反復回数を大幅に削減した。
著者: Teguh Santoso Lembono, Carlos Mastalli, Pierre Fernbach, Nicolas Mansard, Sylvain Calinon
分類: cs.RO, math.OC
原文アブストラクト
In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consistent whole-body trajectory to be stored as the memory of motion. The learning problem is formulated as a regression problem to predict a single-step motion given the desired contact locations, which is used as a building block for producing multi-step motions. The predicted motion is then used as a warm-start for the fast optimal control solver Crocoddyl. We have shown that the approach manages to reduce the required number of iterations to reach the convergence from $\sim$9.5 to only $\sim$3.0 iterations for the single-step motion and from $\sim$6.2 to $\sim$4.5 iterations for the multi-step motion, while maintaining the solution's quality.
