操舵による歩行学習：動的環境における知覚的四足歩行
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments
動的環境での四足歩行を、人間の操舵デモによる模倣学習で高レベル経路生成を、強化学習で低レベル歩容生成を学習する階層型フレームワークPRELUDEで実現した。
著者: Mingyo Seo, Ryan Gupta, Yifeng Zhu, Alexy Skoutnev, Luis Sentis, Yuke Zhu
分類: cs.RO, cs.AI
原文アブストラクト
We tackle the problem of perceptive locomotion in dynamic environments. In this problem, a quadrupedal robot must exhibit robust and agile walking behaviors in response to environmental clutter and moving obstacles. We present a hierarchical learning framework, named PRELUDE, which decomposes the problem of perceptive locomotion into high-level decision-making to predict navigation commands and low-level gait generation to realize the target commands. In this framework, we train the high-level navigation controller with imitation learning on human demonstrations collected on a steerable cart and the low-level gait controller with reinforcement learning (RL). Therefore, our method can acquire complex navigation behaviors from human supervision and discover versatile gaits from trial and error. We demonstrate the effectiveness of our approach in simulation and with hardware experiments. Videos and code can be found at the project page: https://ut-austin-rpl.github.io/PRELUDE.
