Iterative path optimisation for personalised dressing assistance using vision and force information

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

Authors

Colleges, School and Institutes

External organisations

  • Imperial College London

Abstract

We propose an online iterative path optimisation method to enable a Baxter humanoid robot to assist human users to dress. The robot searches for the optimal personalised dressing path using vision and force sensor information: vision information is used to recognise the human pose and model the movement space of upper-body joints; force sensor information is used for the robot to detect external force resistance and to locally adjust its motion. We propose a new stochastic path optimisation method based on adaptive moment estimation. We first compare the proposed method with other path optimisation algorithms on synthetic data. Experimental results show that
the performance of the method achieves the smallest error with fewer iterations and less computation time. We also evaluate real-world data by enabling the Baxter robot to assist real human users with their dressing.

Details

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publication statusPublished - 9 Oct 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016) - Daejeon, Korea, Republic of
Duration: 9 Oct 201614 Oct 2016

Conference

Conference2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016)
CountryKorea, Republic of
CityDaejeon
Period9/10/1614/10/16