Title:
Combining Motion Planning and Optimization for Flexible Robot Manipulation
Combining Motion Planning and Optimization for Flexible Robot Manipulation
Author(s)
Scholz, Jonathan
Stilman, Mike
Stilman, Mike
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Abstract
Robots that operate in natural human environments
must be capable of handling uncertain dynamics and
underspecified goals. Current solutions for robot motion planning
are split between graph-search methods, such as RRT
and PRM which offer solutions to high-dimensional problems,
and Reinforcement Learning methods, which relieve the need to
specify explicit goals and action dynamics. This paper addresses
the gap between these methods by presenting a task-space
probabilistic planner which solves general manipulation tasks
posed as optimization criteria. Our approach is validated in
simulation and on a 7-DOF robot arm that executes several
tabletop manipulation tasks. First, this paper formalizes the
problem of planning in underspecified domains. It then describes
the algorithms necessary for applying this approach to
planar manipulation tasks. Finally it validates the algorithms
on a series of sample tasks that have distinct objectives, multiple
objects with different shapes/dynamics, and even obstacles that
interfere with object motion.
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Date Issued
2010-12
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Text
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Proceedings