User modelling for personalised dressing assistance by humanoid robots using multi-modal information
File(s)
Author(s)
Gao, Yixing
Type
Thesis
Abstract
To enable personalised assistance, assistive robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. Among various tasks in home environments, assistive dressing, which is greatly beneficial to people with upper-body movement limitations, remains a challenging task for humanoid robots. In this thesis, we aim to design, implement, and evaluate user modelling methods which can enable humanoid robots to provide personalised dressing assistance. We begin by proposing a user modelling method using vision information. We use Gaussian mixture models (GMMs) to model the movement space of the human upper-body joints to learn the reachable area of each joint. We enabled a Baxter humanoid robot to plan its dressing motion using the GMMs of the human joints and real-time pose estimation. The dressing assistance is personalised by fulfilling a reachability criterion. To compensate for the disadvantages of using vision information only, we proposed an online iterative path optimisation method based on adaptive moment estimation. We enabled the Baxter robot to search for the optimal personalised dressing path for human users using force information. The dressing assistance is personalised by fulfilling a comfort criterion. Finally, to enable personalised dressing assistance fulfilling both the reachability and the comfort criteria, we proposed a user modelling method using multi-modal information by combining the GMMs of the human upper-body joints with the online iterative path optimisation. Experiments on both the synthetic dataset and the real-world assistive dressing data showed that the proposed method can achieve a balance between the two criteria when searching for the optimal path.
Version
Open Access
Date Issued
2016-09
Date Awarded
2017-01
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Demiris, Yiannis
Sponsor
China Scholarship Council
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)