Human Motion Imitation using Optimal Control with Time-Varying Weights

Shouyo Ishida, Tatsuki Harada, Pamela Carreno-Medrano, Dana Kulic, Gentiane Venture

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

Research in biomechanics hypothesizes that human motion is optimal with respect to an unknown cost function that varies depending on the action and/or task. This unknown cost function is often approximated as the weighted sum of a set of features or basis cost functions. As a person performs a sequence of actions, the weights associated to each of these basis functions are likely to vary over time. Given a human demonstration and the corresponding cost weight trajectory recovered via inverse optimal control (IOC), this paper proposes an optimal control (OC) method that can generate robot motion based on human movement using time-varying cost function weights. By using time-varying weights, the proposed optimal control method can handle changing optimization criteria without segmentation. The method is evaluated both in simulation and with recorded human data. Using human demonstration data, we demonstrate the reproduction of pick-and-place motions with an average end-effector error at the pick place location within 0.82 cm, which is significantly lower than the average trajectory error, indicating that the approach correctly prioritizes reaching the pick and place locations without manual segmentation.

Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
EditorsRobert Babuška
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages608-615
Number of pages8
ISBN (Electronic)9781665417143
ISBN (Print)9781665417150
DOIs
Publication statusPublished - 2021
EventIEEE/RSJ International Conference on Intelligent Robots and Systems 2021 - Online, Prague, Czechia
Duration: 27 Sept 20211 Oct 2021
https://ieeexplore.ieee.org/xpl/conhome/9635848/proceeding (Proceedings)

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems 2021
Abbreviated titleIROS 2021
Country/TerritoryCzechia
CityPrague
Period27/09/211/10/21
Internet address

Keywords

  • Motion generation
  • Optimal control
  • Robot motion strategy

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