Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
//

Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
//

Location

 

1014Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Gao:2016,
author = {Gao, Y and Chang, HJ and Demiris, Y and Gao, Y and Chang, HJ and Demiris, Y},
pages = {4398--4403},
publisher = {IEEE},
title = {Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000391921704063&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - We propose an online iterative path optimisationmethod to enable a Baxter humanoid robot to assist humanusers to dress. The robot searches for the optimal personaliseddressing path using vision and force sensor information: visioninformation is used to recognise the human pose and model themovement space of upper-body joints; force sensor informationis used for the robot to detect external force resistance andto locally adjust its motion. We propose a new stochastic pathoptimisation method based on adaptive moment estimation. Wefirst compare the proposed method with other path optimisationalgorithms on synthetic data. Experimental results show thatthe performance of the method achieves the smallest error withfewer iterations and less computation time. We also evaluatereal-world data by enabling the Baxter robot to assist realhuman users with their dressing.
AU - Gao,Y
AU - Chang,HJ
AU - Demiris,Y
AU - Gao,Y
AU - Chang,HJ
AU - Demiris,Y
EP - 4403
PB - IEEE
PY - 2016///
SP - 4398
TI - Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000391921704063&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/39009
ER -