ベイズ最適化によるハイブリッド軌道最適化を用いた歩行計画
Locomotion Planning through a Hybrid Bayesian Trajectory Optimization
脚式ロボットの歩行計画において、接触スケジュールの選択をベイズ最適化で学習する二段階最適化手法を提案し、単脚ホッピングの計画で有効性を示した。
著者: Tim Seyde, Jan Carius, Ruben Grandia, Farbod Farshidian, Marco Hutter
分類: cs.RO
原文アブストラクト
Locomotion planning for legged systems requires reasoning about suitable contact schedules. The contact sequence and timings constitute a hybrid dynamical system and prescribe a subset of achievable motions. State-of-the-art approaches cast motion planning as an optimal control problem. In order to decrease computational complexity, one common strategy separates footstep planning from motion optimization and plans contacts using heuristics. In this paper, we propose to learn contact schedule selection from high-level task descriptors using Bayesian optimization. A bi-level optimization is defined in which a Gaussian process model predicts the performance of trajectories generated by a motion planning nonlinear program. The agent, therefore, retains the ability to reason about suitable contact schedules, while explicit computation of the corresponding gradients is avoided. We delineate the algorithm in its general form and provide results for planning single-legged hopping. Our method is capable of learning contact schedule transitions that align with human intuition. It performs competitively against a heuristic baseline in predicting task appropriate contact schedules.
