Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport

Zhicheng WANG; Xin ZHAO; Meng Yee (Michael) CHUAH; Zhibin LI; Jun WU; Qiuguo ZHU

doi:10.1631/FITEE.2401070

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Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport

Regular Papers | Updated：2025-11-05

- Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport
- 面向运输代价最小化的鲁棒多步态四足机器人高效学习
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 9, Pages: 1679-1691(2025)
- Affiliations：
  
  1.Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
  2.Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore
  3.Chinese Scholartree Ridge State Key Laboratory, China North Vehicle Research Institute, Beijing 100072, China
  4.Department of Computer Science, University College London, London WC1E, UK
  5.State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  E-mail: willw317@zju.edu.cn
  ‡ Corresponding author
- Funds：
- DOI：10.1631/FITEE.2401070
  CLC： TP242.6;TP18
- Received：16 December 2024，
  
  Revised：2025-05-12，
  
  Published：2025-09
- Accepted：
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Zhicheng WANG, Xin ZHAO, Meng Yee (Michael) CHUAH, et al. Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1679-1691.
DOI：

Zhicheng WANG, Xin ZHAO, Meng Yee (Michael) CHUAH, et al. Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1679-1691. DOI： 10.1631/FITEE.2401070.

摘要

四足机器人能够使用多种步态节律进行移动，每种步态在地形通过性和能量效率方面具有不同特点。通过在不同环境下主动切换调整步态，四足机器人可以实现节能且适应性强的运动策略。本文探讨不同步态参数下基于强化学习的四足机器人运动策略的能量效率和地形通过能力，提出一种训练—整合框架，将习得的单步态运动策略整合为一个高效的多步态运动策略。所得到的控制策略实现了低成本的步态切换和可控的步态。实验结果表明，该多技能策略在保持能量最优的同时，能够实现平滑安全的步态过渡。

Abstract

Quadruped robots are able to exhibit a range of gaits

each with its own traversability and energy efficiency characteristics. By actively coordinating between gaits in different scenarios

energy-efficient and adaptive locomotion can be achieved. This study investigates the performances of learned energy-efficient policies for quadrupedal gaits under different commands. We propose a training–synthesizing framework that integrates learned gait-conditioned locomotion policies into an efficient multiskill locomotion policy. The resulting control policy achieves low-cost smooth switching and controllable gaits. Our results of the learned multiskill policy demonstrate seamless gait transitions while maintaining energy optimality across all commands.

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references

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