Jump to ContentJump to Main Navigation
Show Summary Details
More options …

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
See all formats and pricing
More options …

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.
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2011-06-16 | DOI: https://doi.org/10.2478/s13230-011-0011-y


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


  • [1] Shihua Chen and Jinhu Lü. Parameters identification and synchronization of chaotic systems based upon adaptive control. Physics Letters A, 299:353–358, 2002.Web of ScienceGoogle Scholar

  • [2] D. DeMenthon. Spatio-temporal segmentation of video by hierarchical mean shift analysis. Language, 2, 2002.Google Scholar

  • [3] Daniel M. Dubois. Mathematical foundations of discrete and functional systems with strong and weak anticipations. In Anticipatory Behavior in Adaptive Learning Systems, Lecture Notes in Computer Science, page 107–125. Springer, 2003.Google Scholar

  • [4] V. Guralnik and J. Srivastava. Event detection from time series data. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, page 33–42. ACM, 1999.Google Scholar

  • [5] Mitsuo Kawato. Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9(6):718–727, December 1999.CrossrefGoogle Scholar

  • [6] Daehwan Kim, Jinyoung Song, and Daijin Kim. Simultaneous gesture segmentation and recognition based on forward spotting accumulative hmms. Pattern Recognition, 40(11):3012–3026, November 2007.Web of ScienceGoogle Scholar

  • [7] Christopher A. Kurby and Jeffrey M. Zacks. Segmentation in the perception and memory of events. Trends in Cognitive Sciences, 12(2):72–79, February 2008.Web of ScienceGoogle Scholar

  • [8] R.C. Miall, D. J. Weir, D. M. Wolpert, and J. F. Stein. Is the cerebellum a smith predictor? Journal of Motor Behavior, 25(3):203–216, 1993.CrossrefGoogle Scholar

  • [9] Bruno Nery and Rodrigo Ventura. Online event segmentation in active perception using adaptive strong anticipation. In Alexei V. Samsonovich, George Mason University, Kamilla R. Jóhannsdóttir, Antonio Chella, and Ben Goertzel, editors, Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the Bica Society (BICA-2010), volume 221 of Frontiers in Artificial Intelligence and Applications, page 86–91. IOS Press, 2010.Google Scholar

  • [10] Louis M. Pecora, Thomas L. Carroll, Gregg A. Johnson, and Douglas J. Mar. Fundamentals of synchronization in chaotic systems, concepts, and applications. Chaos, 7(4):520–543, 1997.Google Scholar

  • [11] Erich Prem, Erik Hörtnagl, and Georg Dorffner. Growing event memories for autonomous robots. In Proceedings of the Workshop On Growing Artifacts that Live: Basic Principles and Future Trends, 2002.Google Scholar

  • [12] Marco Ramoni, Paola Sebastiani, and Paul Cohen. Unsupervised clustering of robot activities: a bayesian approach. In Proceedings of the fourth international conference on Autonomous agents (AGENTS’00), page 134–135, 2000.Google Scholar

  • [13] Wolfram Schultz and Anthony Dickinson. Neuronal coding of prediction errors. Annual Review of Neuroscience, 23:473–500, 2000.CrossrefGoogle Scholar

  • [14] N. Stepp and M.T. Turvey. On strong anticipation. Cognitive Systems Research, 11:148–164, 2010.Web of ScienceCrossrefPubMedGoogle Scholar

  • [15] Henning U. Voss. Anticipating chaotic synchronization. Physical review E, 61(5):5115–5119, 2000.Google Scholar

  • [16] Henning U. Voss. Dynamic long-term anticipation of chaotic states. Physical Review Letters, 87(1):14102, July 2001.Google Scholar

  • [17] J.Y.A. Wang and E.H. Adelson. Spatio-temporal segmentation of video data. In SPIE Proceedings Image and Video Processing II, volume 2182, page 120–131, 1994.Google Scholar

  • [18] Daniel M. Wolpert, R. Chris Miallb, and Mitsuo Kawato. Internal models in the cerebellum. Trends in Cognitive Sciences, 2(9):338–347, 1998.Google Scholar

  • [19] Jeffrey M. Zacks, Nicole K. Speer, Khena M. Swallow, Todd S. Braver, and Jeremy R. Reynolds. Event perception: A mind–brain perspective. Psychological Bulletin, 133(2):273–293, 2007.Web of ScienceGoogle Scholar

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.

Export Citation

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

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Henning U. Voss
Physical Review E, 2016, Volume 93, Number 3

Comments (0)

Please log in or register to comment.
Log in