ARMP: 複雑な屋内環境における四足歩行ロボットのための自己回帰型動作計画
ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments
軌道最適化で動作ライブラリを構築し、その多様体を教師あり学習で獲得することで、任意長の物理的に実現可能な動作計画を自己回帰的に生成する四足歩行ロボットのフレームワークを提案した。
著者: Jeonghwan Kim, Tianyu Li, Sehoon Ha
分類: cs.RO
原文アブストラクト
Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settings. Then we learn the motion manifold from the dataset in a supervised learning fashion. We show that the proposed ARMP can generate physically plausible motions for various tasks and situations. We also showcase that our method can be successfully integrated with the recent robot navigation frameworks as a low-level controller and unleash the full capability of legged robots for complex indoor navigation.
