Learning interaction for collaborative tasks with probabilistic movement primitives

Maeda, G., Ewerton, M., Lioutikov, R. , Ben Amor, H., Peters, J. and Neumann, G. (2014) Learning interaction for collaborative tasks with probabilistic movement primitives. In: 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 18 - 20 November 2014, Madrid, Spain.

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Abstract

This paper proposes a probabilistic framework
based on movement primitives for robots that work in collaboration
with a human coworker. Since the human coworker
can execute a variety of unforeseen tasks a requirement of our
system is that the robot assistant must be able to adapt and
learn new skills on-demand, without the need of an expert
programmer. Thus, this paper leverages on the framework
of imitation learning and its application to human-robot interaction
using the concept of Interaction Primitives (IPs).
We introduce the use of Probabilistic Movement Primitives
(ProMPs) to devise an interaction method that both recognizes
the action of a human and generates the appropriate movement
primitive of the robot assistant. We evaluate our method
on experiments using a lightweight arm interacting with a
human partner and also using motion capture trajectories of
two humans assembling a box. The advantages of ProMPs in
relation to the original formulation for interaction are exposed
and compared.

Keywords:Interaction Learning, Movement Primitives, Human-Robot Collaboration
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:25764
Deposited On:02 Feb 2017 16:34

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