Using cluster-based stereotyping to foster human-robot cooperation

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

7 Scopus citations

Abstract

Psychologists note that humans regularly use categories to simplify and speed up the process of person perception [1]. The influence of categorical thinking on interpersonal expectations is commonly referred to as a stereotype. The ability to bootstrap the process of learning about a newly encountered, unknown person is critical for robots interacting in complex and dynamic social situations. This article contributes a novel cluster-based algorithm that allows a robot to create generalized models of its interactive partner. These generalized models, or stereotypes, act as a source of information for predicting the human's behavior and preferences. We show, in simulation and using real robots, that these stereotyped models of the partner can be used to bootstrap the robot's learning about the partner in spite of significant error. The results of this work have potential implications for social robotics, autonomous agents, and possibly psychology.

Original languageEnglish (US)
Title of host publication2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
Pages1615-1622
Number of pages8
DOIs
StatePublished - 2012
Event25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve, Portugal
Duration: Oct 7 2012Oct 12 2012

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
CountryPortugal
CityVilamoura, Algarve
Period10/7/1210/12/12

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Using cluster-based stereotyping to foster human-robot cooperation'. Together they form a unique fingerprint.

Cite this