Change search
ReferencesLink to record
Permanent link

Direct link
Adaptation of an Interactive Robot's Behavior Using Policy Gradient Reinforcement Learning
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS)ORCID iD: 0000-0003-2078-8854
Show others and affiliations
2005 (English)In: Proceedings of the 10th Robotics Symposia, 2005, 319-324 p.Conference paper (Refereed)
Abstract [en]

In this paper we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze-meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.

Place, publisher, year, edition, pages
2005. 319-324 p.
Keyword [en]
policy gradient reinforcement learning, PGRL, human-robot interaction, adaptive behavior, proxemics.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-38202OAI: diva2:436243
10th Robotics Symposia
Available from: 2011-08-22 Created: 2011-08-22 Last updated: 2011-09-12Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Smith, Christian
By organisation
Computer Vision and Active Perception, CVAP
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 17 hits
ReferencesLink to record
Permanent link

Direct link