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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor


Covered by SCOPUS


CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2017: 99.90

Open Access
Online
ISSN
2081-4836
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A dynamical systems approach to online event segmentation in cognitive robotics*

Bruno Nery / Rodrigo Ventura
  • Institute for Systems and Robotics, Instituto Superior Tecnico, Av. Rovisco Pais, 1; 1049-001 Lisboa; PORTUGAL
  • Also visiting scholar at Computer Science Department, Carnegie Mellon University.
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  • Other articles by this author:
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Published Online: 2011-06-16 | DOI: https://doi.org/10.2478/s13230-011-0011-y

Abstract

This paper addresses the problem of segmenting perception in physical robots into meaningful events along time. In structured environments this problem can be approached using domain-specific techniques, but in the general case, as when facing unknown environments, this becomes a non-trivial problem. We propose a dynamical systems approach to this problem, consisting of simultaneously learning a model of the robot's interaction with the environment (robot and world seen as a single, coupled dynamical system), and deriving predictions about its short-term evolution. Event boundaries are detected once synchronization is lost, according to a simple statistical test. An experimental proof of concept of the proposed framework is presented, simulating a simple active perception task of a robot following a ball. The results reported here corroborate the approach, in the sense that the event boundaries are correctly detected.

Keywords: event segmentation; anticipative systems; active perception; cognitive robotics

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About the article


Received: 2011-01-28

Accepted: 2011-05-06

Published Online: 2011-06-16

Published in Print: 2011-03-01


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 2, Issue 1, Pages 18–24, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-011-0011-y.

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© Bruno Nery et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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