Agile and Versatile Robot Locomotion via Kernel-based Residual Learning

Mohammadreza Kasaei, Milo Carroll, Zhaocheng Liu, Alex Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work developed a kernel-based residual learning framework for quadrupedal robotic locomotion. Initially, a kernel neural network is trained with data collected from an MPC controller. Alongside a frozen kernel network, a residual controller network is trained using reinforcement learning to acquire generalized locomotion skills and robustness against external perturbations. The proposed framework successfully learns a robust quadrupedal locomotion controller with high sample efficiency and controllability, which can provide omnidirectional locomotion at continuous velocities. We validated its versatility and robustness on unseen terrains that the expert MPC controller failed to traverse. Furthermore, the learned kernel can produce a range of functional locomotion behaviors and can generalize to unseen gaits.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers
Pages5148-5154
Number of pages7
ISBN (Electronic)9798350323658
ISBN (Print)9798350323665
DOIs
Publication statusPublished - 4 Jul 2023
Event2023 IEEE International Conference on Robotics and Automation - London, United Kingdom
Duration: 29 May 20232 Jun 2023
https://www.icra2023.org

Conference

Conference2023 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23
Internet address

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