Abstract
In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how the same conceptual representation can be used both in a bottom-up approach, in order to learn sequences of actions by imitation learning paradigm, and in a top-down approach, in order to anchor the symbolical representations to the perceptual activities of the robotic system. The proposed architecture has been tested on the robotic system composed of a PUMA 200 industrial manipulator and an anthropomorphic robotic hand. The system demonstrated the ability to learn and imitate a set of movement primitives acquired through the vision system for simple manipulative purposes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alisssandrakis, A., Nehaniv, C.L., Dautenhahn, K.: Solving the correspondence problem between dissimilarly embodied robotic arms using the ALICE imitation mechanism. In: Proceedings of the Second International Symposium on Imitation in Animals & Artifacts, The Society for the Study of Artificial Intelligence and Simulation of Behaviour, pp. 79–92 (2003)
Allen, J.F.: Towards a general theory of action and time. Artificial Intelligence 23(2), 123–154 (1984)
Arbib, M., Rizzolati, G.: Neural expectations: A possible evolutionary path from manual skills to language. Communication and Cognition 29(2-4), 393–424 (1996)
Atkeson, C.G., Schaal, S.: Learning Tasks From A Single Demonstration. In: Proceedings of IEEE-ICRA, Albuquerque, New Mexico, pp. 1706–1712 (1997)
Billard, A.: Imitation: a means to enhance learning of a synthetic protolanguage in autonomous robots. In: Imitation in Animals and Artifacts. MIT Press, Redmond (2002)
Billard, A., Mataric, M.J.: Learning human arm movements by imitation: Evalutation of a biologically inspired connectionist architecture. Robotics and Autonomous System 37, 145–160 (2001)
Chella, A., Frixione, M., Gaglio, S.: Anchoring symbols to conceptual spaces: the case of dynamic scenarios. Robotics and Autonomous Systems, special issue on Perceptual Anchoring 43(2-3), 175–188 (2003)
Chella, A., Infantino, I., Dindo, H., Macaluso, I.: A Posture Sequence Learning System for an Anthropomorphic Robotic Hand. Robotics and Autonomous Systems 47, 143–152 (2004)
Coradeschi, S., Saffiotti, A.: Perceptual anchoring of symbols for action. In: Proceedings of the 17th International Conference on Artificial Intelligence IJCAI 2001, pp. 407–412. Morgan Kaufmann, San Mateo (2001)
Dautenhahn, K., Nehaniv, C.L.: The agent-based perspective on imitation. In: Imitation in Animals and Artifacts. MIT Press, Cambridge (2002)
Gärdenfors, P.: Conceptual Spaces. MIT Press/Bradford Books (2000)
Ijspeert, J.A., Nakanishi, J., Schaal, S.: Movement imitation with nonlinear dynamical systems in humanoid robots. In: Proceedings of Intl. Conf. on Robotics and Automation (ICRA 2002), Wahington (2002)
Ogawarw, K., Takamatsu, J., Kimura, H., Ikeuchi, K.: Generation of a task model by integrating multiple observations of human demonstrations. In: Proceedings of IEEE-ICRA, Washington, DC, Usa (May 2002)
Reiter, R.: Knowledge in Action: Logical Foundations for Describing and Implementing Dynamical Systems. MIT Press/Bradford Books (2001)
Rittscher, J., Blake, A., Hoogs, A., Stein, G.: Mathematical modelling of animate and intentional motion. Philosophical Transactions: Biological Sciences (The Royal Society) 358, 475–490 (2003)
Sanger, T.D.: Human arm movements described by a low dimensional superposition of principal components. The Journal of Neuroscience 20(3), 1066–1072 (2000)
Schaal, S.: Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences 3, 233–242 (1999)
Schaal, S., Ijspeert, A.J., Billard, A.: Computational Approaches to Motor Learning by Imitation. Philosophical Transactions: Biological Sciences (The Royal Society) 358, 537–547 (2003)
Ude, A., Shibata, T., Atkeson, C.G.: Real time visual system for interaction with a humanoid robot. Robotics and Autonomous System 37, 115–126 (2001)
Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philosophical Transactions: Biological Sciences (The Royal Society) 358, 593–602 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chella, A., Dindo, H., Infantino, I. (2005). Anchoring by Imitation Learning in Conceptual Spaces. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_50
Download citation
DOI: https://doi.org/10.1007/11558590_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29041-4
Online ISBN: 978-3-540-31733-3
eBook Packages: Computer ScienceComputer Science (R0)