Title:
Haptic Simulation for Robot-Assisted Dressing
Haptic Simulation for Robot-Assisted Dressing
Author(s)
Yu, Wenhao
Kapusta, Ariel
Tan, Jie
Kemp, Charles C.
Turk, Greg
Liu, C. Karen
Kapusta, Ariel
Tan, Jie
Kemp, Charles C.
Turk, Greg
Liu, C. Karen
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Abstract
There is a considerable need for assistive dressing
among people with disabilities, and robots have the potential
to fulfill this need. However, training such a robot would
require extensive trials in order to learn the skills of assistive
dressing. Such training would be time-consuming and require
considerable effort to recruit participants and conduct trials.
In addition, for some cases that might cause injury to the
person being dressed, it is impractical and unethical to perform
such trials. In this work, we focus on a representative dressing
task of pulling the sleeve of a hospital gown onto a person’s
arm. We present a system that learns a haptic classifier for the
outcome of the task given few (2-3) real-world trials with one
person. Our system first optimizes the parameters of a physics
simulator using real-world data. Using the optimized simulator,
the system then simulates more haptic sensory data with noise
models that account for randomness in the experiment. We
then train hidden Markov Models (HMMs) on the simulated
haptic data. The trained HMMs can then be used to classify
and predict the outcome of the assistive dressing task based
on haptic signals measured by a real robot’s end effector. This
system achieves 92.83% accuracy in classifying the outcome
of the robot-assisted dressing task with people not included in
simulation optimization. We compare our classifiers to those
trained on real-world data. We show that the classifiers from
our system can categorize the dressing task outcomes more
accurately than classifiers trained on ten times more real data.
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Date Issued
2017
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Proceedings