Physical interaction learning: Behavior adaptation in cooperative human-robot tasks involving physical contact

Shuhei Ikemoto, Heni Ben Amor, Takashi Minato, Hiroshi Ishiguro, Bernhard Jung

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

12 Scopus citations

Abstract

In order for humans and robots to engage in direct physical interaction several requirements have to be met. Among others, robots need to be able to adapt their behavior in order to facilitate the interaction with a human partner. This can be achieved using machine learning techniques. However, most machine learning scenarios to-date do not address the question of how learning can be achieved for tightly coupled, physical touch interactions between the learning agent and a human partner. This paper presents an example for such human in-the-loop learning scenarios and proposes a computationally cheap learning algorithm for this purpose. The efficiency of this method is evaluated in an experiment, where human care givers help an android robot to stand up.

Original languageEnglish (US)
Title of host publicationRO-MAN 2009 - 18th IEEE International Symposium on Robot and Human Interactive
Pages504-509
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event18th IEEE International Symposium on Robot and Human Interactive, RO-MAN 2009 - Toyama, Japan
Duration: Sep 27 2009Oct 2 2009

Publication series

NameProceedings - IEEE International Workshop on Robot and Human Interactive Communication

Other

Other18th IEEE International Symposium on Robot and Human Interactive, RO-MAN 2009
Country/TerritoryJapan
CityToyama
Period9/27/0910/2/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction

Fingerprint

Dive into the research topics of 'Physical interaction learning: Behavior adaptation in cooperative human-robot tasks involving physical contact'. Together they form a unique fingerprint.

Cite this