Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

Autor:	Da, Xingye, Xie, Zhaoming, Hoeller, David, Boots, Byron, Anandkumar, Animashree, Zhu, Yuke, Babich, Buck, Garg, Animesh
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: supplementary video: https://youtu.be/JJOmFZKpYTo
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.10019 Zobrazit plný text záznamu View this record from Arxiv