ValueNetQP：脚式移動のための学習型ワンステップ最適制御
ValueNetQP: Learned one-step optimal control for legged locomotion
問題の価値関数の勾配とヘッセ行列を学習し、1ステップの二次計画で予測制御を高速化する手法を提案。摩擦円錐などの制約を満たしつつ、実機の四足ロボットでトロットとバウンド動作を実現した。
著者: Julian Viereck, Avadesh Meduri, Ludovic Righetti
分類: cs.RO
原文アブストラクト
Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for high dynamics locomotion tasks. We demonstrate the capability of our method in simulation and on a real quadruped robot performing trotting and bounding motions.
