接触に適応する四脚ロボット制御の階層学習フレームワーク
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
モデルベース制御と強化学習を組み合わせ、環境変化に応じてプリミティブを選択する階層型制御器を四脚ロボット向けに学習させ、省エネ性と頑健性を向上させた。
著者: Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg
分類: cs.RO, cs.LG
原文アブストラクト
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute the primitives. Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85~percent more energy efficient and is more robust compared to baseline methods. We also deploy the controller on a physical robot without any randomization or adaptation scheme.
