Learning interaction for collaborative tasks with probabilistic movement primitives

Guilherme Maeda, Marco Ewerton, Rudolf Lioutikov, Heni Ben Amor, Jan Peters, Gerhard Neumann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
PublisherIEEE Computer Society
Pages527-534
Number of pages8
ISBN (Electronic)9781479971749
DOIs
StatePublished - Feb 12 2015
Event2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 - Madrid, Spain
Duration: Nov 18 2014Nov 20 2014

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2015-February
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Other

Other2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
CountrySpain
CityMadrid
Period11/18/1411/20/14

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

Cite this

Maeda, G., Ewerton, M., Lioutikov, R., Ben Amor, H., Peters, J., & Neumann, G. (2015). Learning interaction for collaborative tasks with probabilistic movement primitives. In 2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 (pp. 527-534). [7041413] (IEEE-RAS International Conference on Humanoid Robots; Vol. 2015-February). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2014.7041413