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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

A Novel Framework for Adaptive Quadruped Robot Locomotion Learning in Uncertain Environments

verfasst von : Mengyuan Li, Bin Guo, Kaixing Zhao, Ruonan Xu, Sicong Liu, Sitong Mao, Shunbo Zhou, Qiaobo Xu, Zhiwen Yu

Erschienen in: Green, Pervasive, and Cloud Computing

Verlag: Springer Nature Singapore

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Abstract

Learning diverse and flexible locomotion strategies in uncertain environments has been a longstanding challenge for quadruped robots. Although recent progress in domain randomization has partially tackled this difficulty by training policies on a wide range of potential factors, there is still a great need for improving efficiency. In this paper, we propose a novel framework for adaptive quadruped robot locomotion learning in uncertain environments. Our method is based on data-efficient reinforcement learning and learns simulation parameters iteratively. We also propose a novel Sampling-Interval-Adaptive Identification (SIAI) strategy that uses historical parameters to optimize sampling distribution and then improve identification accuracy. Final evaluations based on multiple robotic locomotion tasks showed superiority of our method over baselines.

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Vorheriges Kapitel A Study of WSN Localization Based on the Enhanced NGO Algorithm
Nächstes Kapitel NLP-Based Test Co-evolution Prediction for IoT Application Maintenance
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Metadaten
Titel
A Novel Framework for Adaptive Quadruped Robot Locomotion Learning in Uncertain Environments
verfasst von
Mengyuan Li
Bin Guo
Kaixing Zhao
Ruonan Xu
Sicong Liu
Sitong Mao
Shunbo Zhou
Qiaobo Xu
Zhiwen Yu
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Green, Pervasive, and Cloud Computing

Print ISBN: 978-981-9998-95-1

Electronic ISBN: 978-981-9998-96-8

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-9896-8_10

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