環境変化に適応するネズミ型ロボットの強化学習による歩行生成
Locomotion Generation for a Rat Robot based on Environmental Changes via Reinforcement Learning
小型四足ロボットNeRmoの限られたセンサ情報からフーリエ変換に基づく特徴抽出を行い、多機能報酬設計により坂道・階段・螺旋階段など異なる地形で適応的な歩行を生成する強化学習手法を提案した。
著者: Xinhui Shan, Yuhong Huang, Zhenshan Bing, Zitao Zhang, Xiangtong Yao, Kai Huang, Alois Knoll
分類: cs.RO
原文アブストラクト
This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimental platform. Due to the constrained volume, small-size quadruped robots typically possess fewer and weaker sensors, resulting in difficulty in accurately perceiving and responding to environmental changes. In this context, insufficient and imprecise feedback data from sensors makes it difficult to generate adaptive locomotion based on reinforcement learning. To overcome these challenges, this paper proposes a novel reinforcement learning approach that focuses on extracting effective perceptual information to enhance the environmental adaptability of small-size quadruped robots. According to the frequency of a robot's gait stride, key information of sensor data is analyzed utilizing sinusoidal functions derived from Fourier transform results. Additionally, a multifunctional reward mechanism is proposed to generate adaptive locomotion in different tasks. Extensive simulations are conducted to assess the effectiveness of the proposed reinforcement learning approach in generating rat robot locomotion in various environments. The experiment results illustrate the capability of the proposed approach to maintain stable locomotion of a rat robot across different terrains, including ramps, stairs, and spiral stairs.
