視覚誘導型四足歩行のためのCPG強化学習
Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion
視覚情報と中枢パターン生成器（CPG）を深層強化学習に統合し、四足ロボットの歩行制御を学習させるフレームワークを提案。シミュレーションから実機への転移で、エネルギー効率やノイズ・遅延への頑健性が向上することを示した。
著者: Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
We present a framework for learning visually-guided quadruped locomotion by integrating exteroceptive sensing and central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework. Through both exteroceptive and proprioceptive sensing, the agent learns to coordinate rhythmic behavior among different oscillators to track velocity commands, while at the same time override these commands to avoid collisions with the environment. We investigate several open robotics and neuroscience questions: 1) What is the role of explicit interoscillator couplings between oscillators, and can such coupling improve sim-to-real transfer for navigation robustness? 2) What are the effects of using a memory-enabled vs. a memory-free policy network with respect to robustness, energy-efficiency, and tracking performance in sim-to-real navigation tasks? 3) How do animals manage to tolerate high sensorimotor delays, yet still produce smooth and robust gaits? To answer these questions, we train our perceptive locomotion policies in simulation and perform sim-to-real transfers to the Unitree Go1 quadruped, where we observe robust navigation in a variety of scenarios. Our results show that the CPG, explicit interoscillator couplings, and memory-enabled policy representations are all beneficial for energy efficiency, robustness to noise and sensory delays of 90 ms, and tracking performance for successful sim-to-real transfer for navigation tasks. Video results can be found at https://youtu.be/wpsbSMzIwgM.
