  Robots can provide assistance to a human by moving objects to locations
around the person's body. With a well chosen initial configuration, a robot can
better reach locations important to an assistive task despite model error, pose
uncertainty and other sources of variation. However, finding effective
configurations can be challenging due to complex geometry, a large number of
degrees of freedom, task complexity and other factors. We present task-centric
optimization of robot configurations (TOC), which is an algorithm that finds
configurations from which the robot can better reach task-relevant locations
and handle task variation. Notably, TOC can return more than one configuration
that when used sequentially enable a simulated assistive robot to reach more
task-relevant locations. TOC performs substantial offline computation to
generate a function that can be applied rapidly online to select robot
configurations based on current observations. TOC explicitly models the task,
environment, and user, and implicitly handles error using representations of
robot dexterity. We evaluated TOC in simulation with a PR2 assisting a user
with 9 assistive tasks in both a wheelchair and a robotic bed. TOC had an
overall average success rate of 90.6\% compared to 50.4\%, 43.5\%, and 58.9\%
for three baseline methods from literature. We additionally demonstrate how TOC
can find configurations for more than one robot and can be used to assist in
designing or optimizing environments.
