堅牢なロボット歩行: 小さな罠を越える俊敏な移動の学習
Robust Robot Walker: Learning Agile Locomotion over Tiny Traps
固有感覚入力のみを用いた二段階学習フレームワークにより、四足ロボットが小さな障害物を安定して通過できるようにした研究。
著者: Shaoting Zhu, Runhan Huang, Linzhan Mou, Hang Zhao
分類: cs.RO, cs.AI
原文アブストラクト
Quadruped robots must exhibit robust walking capabilities in practical applications. In this work, we propose a novel approach that enables quadruped robots to pass various small obstacles, or "tiny traps". Existing methods often rely on exteroceptive sensors, which can be unreliable for detecting such tiny traps. To overcome this limitation, our approach focuses solely on proprioceptive inputs. We introduce a two-stage training framework incorporating a contact encoder and a classification head to learn implicit representations of different traps. Additionally, we design a set of tailored reward functions to improve both the stability of training and the ease of deployment for goal-tracking tasks. To benefit further research, we design a new benchmark for tiny trap task. Extensive experiments in both simulation and real-world settings demonstrate the effectiveness and robustness of our method. Project Page: https://robust-robot-walker.github.io/
