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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