重心モデルで実現する多様環境対応の四足歩行
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model
高忠実度な物理シミュレータの代わりに単純な重心モデルと強化学習を組み合わせ、接地力を二次計画法で実現することで、平地歩行から平均台歩行まで汎化し実機転移も可能な四足歩行制御を実現した。
著者: Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel van de Panne
分類: cs.RO, cs.LG
原文アブストラクト
Model-free reinforcement learning (RL) for legged locomotion commonly relies on a physics simulator that can accurately predict the behaviors of every degree of freedom of the robot. In contrast, approximate reduced-order models are commonly used for many model predictive control strategies. In this work we abandon the conventional use of high-fidelity dynamics models in RL and we instead seek to understand what can be achieved when using RL with a much simpler centroidal model when applied to quadrupedal locomotion. We show that RL-based control of the accelerations of a centroidal model is surprisingly effective, when combined with a quadratic program to realize the commanded actions via ground contact forces. It allows for a simple reward structure, reduced computational costs, and robust sim-to-real transfer. We show the generality of the method by demonstrating flat-terrain gaits, stepping-stone locomotion, two-legged in-place balance, balance beam locomotion, and direct sim-to-real transfer.
