Title:
Optimization of Robot Configurations for Assistive Tasks
Optimization of Robot Configurations for Assistive Tasks
Author(s)
Kapusta, Ariel
Kemp, Charles C.
Kemp, Charles C.
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Abstract
Robots can provide assistance with activities of daily
living (ADLs) to humans with motor impairments. Specialized
robots, such as desktop robotic feeding systems, have been
successful for specific assistive tasks when placed in fixed and
designated positions with respect to the user. General-purpose
mobile manipulators could act as a more versatile form of
assistive technology, able to perform many tasks, but selecting a
configuration for the robots from which to perform a task can be
challenging due to the high number of degrees of freedom of the
robots and the complexity of the tasks. As with the specialized,
fixed robots, once in a good configuration, another system or the
user can provide the fine control to perform the details of the task.
In this short paper, we present Task-centric Optimization of robot
Configurations (TOC), a method for selecting configurations for
a PR2 and a robotic bed to allow the PR2 to provide effective
assistance with ADLs. TOC builds upon previous work, Task-centric
initial Configuration Selection (TCS), addressing some
of the limitations of TCS. Notable alterations are selecting
configurations from the continuous configuration space using
a Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
optimization, introducing a joint-limit-weighted manipulability
term, and changing the framework to move all optimization
offline and using function approximation at run-time. To evaluate
TOC, we created models of 13 activities of daily living (ADLs) and
compared TOC’s and TCS’s performance with these 13 assistive
tasks in a computer simulation of a PR2, a robotic bed, and a
model of a human body. TOC performed as well or better than
TCS in most of our tests against state estimation error. We also
implemented TOC on a real PR2 and a real robotic bed and
found that from the TOC-selected configuration the PR2 could
reach all task-relevant goals on a mannequin on the bed.
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Date Issued
2016