Title:
Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter
Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter
Author(s)
Park, Daehyung
Kapusta, Ariel
Kim, You Keun
Rehg, James M.
Kemp, Charles C.
Kapusta, Ariel
Kim, You Keun
Rehg, James M.
Kemp, Charles C.
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Abstract
Often in highly-cluttered environments, a robot
can observe the exterior of the environment with ease, but
cannot directly view nor easily infer its detailed internal
structure (e.g., dense foliage or a full refrigerator shelf). We
present a data-driven approach that greatly improves a robot’s
success at reaching to a goal location in the unknown interior of
an environment based on observable external properties, such
as the category of the clutter and the locations of openings
into the clutter (i.e., apertures). We focus on the problem of
selecting a good initial configuration for a manipulator when
reaching with a greedy controller. We use density estimation
to model the probability of a successful reach given an initial
condition and then perform constrained optimization to find
an initial condition with the highest estimated probability of
success. We evaluate our approach with two simulated robots
reaching in clutter, and provide a demonstration with a real PR2
robot reaching to locations through random apertures. In our
evaluations, our approach significantly outperformed two alter-
native approaches when making two consecutive reach attempts
to goals in distinct categories of unknown clutter. Notably, our
approach only uses sparse readily-apparent features.
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Date Issued
2014-09
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Text
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Post-print
Proceedings
Proceedings