Gaze-assisted adaptive motion scaling optimization using graded and preference based bayesian approaches
File(s)
Accepted version
Author(s)
Gras, G
Seneci, Carlo
Giataganas, Petros
Yang, Guang Zhong
Type
Conference Paper
Abstract
A key component to the success of master-slave
surgical systems is the quality of the master interface used
to relay the surgeon’s instructions to the slave robot. In
previous work the authors developed a gaze-assisted intention
recognition scheme, allowing the system to dynamically adapt
the motion scaling based on where the user is trying to reach.
This allowed users to perform tasks significantly more quickly
and with less need for clutching. However, the system possessed
a number of core parameters that were manually optimized,
potentially providing a non-optimal solution depending on the
user. This paper presents a Bayesian approach to the problem of
optimizing a human-robot interface in a user-specific manner.
Two Bayesian optimization methods are studied: one in which
users are asked to grade robot behavior for a given set
of parameters, and one where only preference relative to
other parameter sets is expressed. The performance of these
optimizations is evaluated in a blind comparison user study,
demonstrating that the optimized parameters are preferred to
the manually optimized ones in over 90% of cases after only 12
test samples. These parameters are further shown to perform
at least as well as the manually optimized ones in all cases, and
showing statistically significant improvement in the case of the
graded optimization.
surgical systems is the quality of the master interface used
to relay the surgeon’s instructions to the slave robot. In
previous work the authors developed a gaze-assisted intention
recognition scheme, allowing the system to dynamically adapt
the motion scaling based on where the user is trying to reach.
This allowed users to perform tasks significantly more quickly
and with less need for clutching. However, the system possessed
a number of core parameters that were manually optimized,
potentially providing a non-optimal solution depending on the
user. This paper presents a Bayesian approach to the problem of
optimizing a human-robot interface in a user-specific manner.
Two Bayesian optimization methods are studied: one in which
users are asked to grade robot behavior for a given set
of parameters, and one where only preference relative to
other parameter sets is expressed. The performance of these
optimizations is evaluated in a blind comparison user study,
demonstrating that the optimized parameters are preferred to
the manually optimized ones in over 90% of cases after only 12
test samples. These parameters are further shown to perform
at least as well as the manually optimized ones in all cases, and
showing statistically significant improvement in the case of the
graded optimization.
Date Issued
2018-05-21
Date Acceptance
2018-01-12
Copyright Statement
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Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/P012779/1
Source
International Conference on Robotics and Automation 2018
Place of Publication
IEEE
Publication Status
Accepted
Start Date
2018-05-21
Finish Date
2018-05-25
Coverage Spatial
Brisbane, Austrailia