Ding, J, Xiao, X, Tsagarakis, N et al. (1 more author) (2021) Robust Gait Synthesis Combining Constrained Optimization and Imitation Learning. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 25-29 Oct 2020, Las Vegas, NV, USA. IEEE , pp. 3473-3480. ISBN 978-1-7281-6213-3
Abstract
Despite plenty of motion planning strategies have been proposed for bipedal locomotion, enhancing the walking robustness in real-world environments is still an open question. This paper focuses on robust body and leg trajectories synthesis through integrating constrained optimization with imitation learning. Specifically, we first propose a Quadratically Constrained Quadratic Programming (QCQP) algorithm to make use of the ankle strategy and stepping strategy. Based on the Linear Inverted Pendulum (LIP) model, body motion can be determined by the modulated Center of Pressure (CoP) position and step parameters (including step location and step duration). After that, we exploit an imitation learning approach Kernelized Movement Primitives (KMP) to plan robot leg motions, which allows for adapting the learned motion patterns to new situations (e.g., passing through various desired points) in a straightforward manner. Several LIP simulations and whole-body dynamic simulations demonstrate that higher walking robustness can be achieved using our framework.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Oct 2020 14:56 |
Last Modified: | 16 Oct 2023 15:33 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS45743.2020.9341146 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167162 |