Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information

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Title: Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information
Author(s): Gao, Y
Chang, HJ
Demiris, Y
Item Type: Conference Paper
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.
Publication Date: 14-Oct-2016
Date of Acceptance: 1-Jul-2016
URI: http://hdl.handle.net/10044/1/39009
Publisher: IEEE
Copyright Statement: This paper is embargoed until publication.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 612139
Conference Name: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publication Status: Accepted
Start Date: 2016-10-10
Finish Date: 2016-10-14
Conference Place: Daejeon, Korea
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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