Imitation learning for coordinated human–robot collaboration based on hidden state-space models

Wang, Likun; Wang, Guoyan; Jia, Shuya; Turner, Alison; Ratchev, Svetan

Abstract

This paper proposes a novel coordinated human–robot collaboration framework based on the hidden state-space model, which probabilistically clones the human behaviour and presents dynamic features in a nonparametric form. Derived from the filter prediction techniques and the theory of exact moment matching, this framework could provide an analytical approximation of the posterior distribution, and hence infer the hidden state variables of the collaborative robot given the external observation and its uncertainties. Not akin to the other cutting-edge movement-primitive based algorithms or coordinated human–robot collaboration methods, our collaboration framework not only preserves the adaptation functionalities of imitation learning but also propagates state variables and their uncertainties during real-time coordinated implementation. By leveraging on the binary Gaussian process classification, additional functionality, such as multiple task recognition is proposed to enhance the generalisation capability of our framework. The application feasibility is verified from both theoretical comparison simulation and real-world experiments.

Citation

Wang, L., Wang, G., Jia, S., Turner, A., & Ratchev, S. (2022). Imitation learning for coordinated human–robot collaboration based on hidden state-space models. Robotics and Computer-Integrated Manufacturing, 76, Article 102310. https://doi.org/10.1016/j.rcim.2021.102310

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2021
• Online Publication Date: Feb 2, 2022
• Publication Date: Aug 1, 2022
• Deposit Date: Feb 23, 2022
• Publicly Available Date: Feb 3, 2023
• Journal: Robotics and Computer-Integrated Manufacturing
• Print ISSN: 0736-5845
• Electronic ISSN: 1879-2537
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 76
• Article Number: 102310
• DOI: https://doi.org/10.1016/j.rcim.2021.102310
• Keywords: Industrial and Manufacturing Engineering; Computer Science Applications; General Mathematics; Software; Control and Systems Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/7504475
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0736584521001903?via%3Dihub