Autonomous Locomotion of a Rat Robot Based on Model-free Reinforcement Learning

Zitao Zhang, Yuhong Huang, Zijian Zhao, Zhenshan Bing, Chenglin Cai, Alois Knoll, Kai Huang

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

The rat robot is a soft compact quadrupedal robot with the same size as real rats. It is difficult for such robots to learn effective motions on complex terrain owing to their underactuated nature and limited sensors. This paper proposes a novel approach for the rat robot to learn adaptive motion on rugged terrain based on reinforcement learning. The training architecture is designed for the rat robot's nonlinear control structure. In order to improve perceptual efficiency, we gather and compress perception information based on sensor data observations in time clusters during robot walking. Our proposed method demonstrates excellent exploration of complex effector space and nonlinear dynamics of the rat robot to adapt to challenging terrain. We evaluate the efficacy of our approach on a varied set of scenarios, which include various obstacles and terrain undulations and physical validation is performed. Our results show that our approach effectively achieves efficient motions on complex terrains designed for small-sized robots and outperforms other benchmark algorithms.

OriginalspracheEnglisch
TitelICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten339-344
Seitenumfang6
ISBN (elektronisch)9798350385724
DOIs
PublikationsstatusVeröffentlicht - 2024
Veranstaltung9th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2024 - Tokyo, Japan
Dauer: 8 Juli 202410 Juli 2024

Publikationsreihe

NameICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics

Konferenz

Konferenz9th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2024
Land/GebietJapan
OrtTokyo
Zeitraum8/07/2410/07/24

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