ミニチータと猫の立ち直り：ロボットアクロバットのための機械学習と軌道最適化の事例研究
Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics
猫が落下時に足から着地する動作に着想を得て、軌道最適化と機械学習を組み合わせ、ミニチータ四足ロボットを落下から足で着地させる制御器を設計した研究。
著者: Vince Kurtz, He Li, Patrick M. Wensing, Hai Lin
分類: cs.RO
原文アブストラクト
Seemingly in defiance of basic physics, cats consistently land on their feet after falling. In this paper, we design a controller that lands the Mini Cheetah quadruped robot on its feet as well. Specifically, we explore how trajectory optimization and machine learning can work together to enable highly dynamic bioinspired behaviors. We find that a reflex approach, in which a neural network learns entire state trajectories, outperforms a policy approach, in which a neural network learns a mapping from states to control inputs. We validate our proposed controller in both simulation and hardware experiments, and are able to land the robot on its feet from falls with initial pitch angles between -90 and 90 degrees.
