Modeling Human and Robot Behavior During Dressing Tasks

Clegg, Alexander William

Title:

Modeling Human and Robot Behavior During Dressing Tasks

dc.contributor.advisor	Liu, Cheng-Yun Karen
dc.contributor.advisor	Turk, Greg
dc.contributor.author	Clegg, Alexander William
dc.contributor.committeeMember	Kemp, Charlie
dc.contributor.committeeMember	Rossignac, Jarek
dc.contributor.committeeMember	Chernova, Sonia
dc.contributor.department	Interactive Computing
dc.date.accessioned	2020-09-08T12:40:01Z
dc.date.available	2020-09-08T12:40:01Z
dc.date.created	2019-08
dc.date.submitted	August 2019
dc.date.updated	2020-09-08T12:40:01Z
dc.description.abstract	Human dressing assistance tasks present a multitude of privacy, safety, and independence concerns for the daily lives of a vast number of individuals across the world, providing strong motivation for the application of assistive robotics to these tasks. Additionally, the challenge of manually generating animations in which virtual characters interact with animated or simulated garments has resulted in the noticeable absence of such scenes in existing video games and animated films, motivating the application of automated motion synthesis techniques to this domain. However, cloth dynamics are complex and predicting the results of planned interactions with a garment can be challenging, which makes manual controller design difficult and makes the use of feedback control strategies an attractive alternative. The focus of this thesis is the development of a set of techniques for behavior modeling and motion synthesis in the space of human dressing. We first consider motion synthesis primarily in the space of self-dressing. We propose a kinematic motion synthesis technique which automatically computes the motion of a virtual character while successfully executing a dressing task with a simulated garment. Next, we explore the impact of haptic (touch) observation modes on the self-dressing task and present a deep reinforcement learning (DRL) approach to navigating simulated garments. We then present a unified DRL approach to self-dressing motion synthesis in which neural network controllers are trained via Trust Region Policy Optimization (TRPO). Finally, we investigate the extension of haptic aware feedback control and DRL to robot assisted dressing. We present a universal policy method for modeling human dressing behavior under variations in capability including: muscle weakness, Dyskinesia, and limited range of motion. Using this method and behavior model, we demonstrate the discovery of successful strategies for a robot to assist humans with a variety of capability limitations.
dc.description.degree	Ph.D.
dc.identifier.uri	http://hdl.handle.net/1853/63506
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Animation
dc.subject	Cloth
dc.subject	Robot
dc.subject	Physics simulation
dc.subject	Dressing
dc.subject	Deep reinforcement learning
dc.subject	Motion synthesis
dc.subject	Haptics
dc.subject	Activities of daily living
dc.subject	Garment
dc.subject	Control
dc.subject	Modeling capability
dc.subject	Human
dc.title	Modeling Human and Robot Behavior During Dressing Tasks
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Turk, Greg
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Interactive Computing
relation.isAdvisorOfPublication	1361247d-c446-453b-8b4a-8e87c3d4210b
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	aac3f010-e629-4d08-8276-81143eeaf5cc
thesis.degree.level	Doctoral