Probabilistic segmentation applied to an assembly task

Lioutikov, R., Neumann, G., Maeda, G. and Peters, J. (2015) Probabilistic segmentation applied to an assembly task. In: 15th IEEE-RAS International Conference on Humanoid Robots, 3 - 5 November 2015, Korea Institute of Science and Technology (KIST), Seoul, Korea (South).

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Abstract

Movement primitives are a well established approach
for encoding and executing robot movements. While
the primitives themselves have been extensively researched, the
concept of movement primitive libraries has not received as
much attention. Libraries of movement primitives represent
the skill set of an agent and can be queried and sequenced in
order to solve specific tasks. The goal of this work is to segment
unlabeled demonstrations into an optimal set of skills. Our
novel approach segments the demonstrations while learning
a probabilistic representation of movement primitives. The
method differs from current approaches by taking advantage of
the often neglected, mutual dependencies between the segments
contained in the demonstrations and the primitives to be encoded.
Therefore, improving the combined quality of both segmentation
and skill learning. Furthermore, our method allows
incorporating domain specific insights using heuristics, which
are subsequently evaluated and assessed through probabilistic
inference methods. We demonstrate our method on a real robot
application, where the robot segments demonstrations of a chair
assembly task into a skill library. The library is subsequently
used to assemble the chair in an order not present in the
demonstrations.

Keywords:Movement Segmentation
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:25751
Deposited On:02 Feb 2017 16:39

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