Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

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

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Zhang:2017:10.1109/IROS.2017.8206206,
author = {Zhang, F and Cully, A and Demiris, YIANNIS},
doi = {10.1109/IROS.2017.8206206},
publisher = {IEEE},
title = {Personalized Robot-assisted Dressing using User Modeling in Latent Spaces},
url = {http://dx.doi.org/10.1109/IROS.2017.8206206},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Robots have the potential to provide tremendous support to disabled and elderly people in their everyday tasks, such as dressing. Many recent studies on robotic dressing assistance usually view dressing as a trajectory planning problem. However, the user movements during the dressing process are rarely taken into account, which often leads to the failures of the planned trajectory and may put the user at risk. The main difficulty of taking user movements into account is caused by severe occlusions created by the robot, the user, and the clothes during the dressing process, which prevent vision sensors from accurately detecting the postures of the user in real time. In this paper, we address this problem by introducing an approach that allows the robot to automatically adapt its motion according to the force applied on the robot's gripper caused by user movements. There are two main contributions introduced in this paper: 1) the use of a hierarchical multi-task control strategy to automatically adapt the robot motion and minimize the force applied between the user and the robot caused by user movements; 2) the online update of the dressing trajectory based on the user movement limitations modeled with the Gaussian Process Latent Variable Model in a latent space, and the density information extracted from such latent space. The combination of these two contributions leads to a personalized dressing assistance that can cope with unpredicted user movements during the dressing while constantly minimizing the force that the robot may apply on the user. The experimental results demonstrate that the proposed method allows the Baxter humanoid robot to provide personalized dressing assistance for human users with simulated upper-body impairments.
AU - Zhang,F
AU - Cully,A
AU - Demiris,YIANNIS
DO - 10.1109/IROS.2017.8206206
PB - IEEE
PY - 2017///
SN - 2153-0866
TI - Personalized Robot-assisted Dressing using User Modeling in Latent Spaces
UR - http://dx.doi.org/10.1109/IROS.2017.8206206
UR - http://hdl.handle.net/10044/1/56559
ER -