Abstract
This paper presents a novel robot behavior learning method based on Adaptive Resonance Theory (ART) neural network and cross-modality learning. We introduce the concept of classification learning and propose a new representation of observed behavior. Compared with previous robot behavior learning methods, this method has the property of learning a new behavior while at the same time preserving prior learned behaviors. Moreover, visual information and audio information are integrated to form a unified percept of the observed behavior, which facilitates robot behavior learning. We implement this learning method on a humanoid robot head for behavior learning and experimental results demonstrate the effectiveness of this method.
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.
References
Bakker, P., Kuniyoshi, Y.: Robot See, Robot Do: An Overview of Robot Imitation. In: AISB 1996 Workshop on Learning in Robots and Animals, pp. 3–11 (1996)
Amit, R., Matarić, M.: Learning Movement Sequences from Demonstration. In: Proceedings of the 2nd International Conference on Development and Learning, pp. 203–208 (2002)
Hovland, G.E., Sikka, P., McCarragher, B.J.: Skill Acquisition from Human Demonstration Using a Hidden Markov Model. In: IEEE International Conference on Robotics and Automation, vol. 3, pp. 2706–2711 (1996)
Ito, M., Tani, J.: On-line Imitative Interaction with a Humanoid Robot Using a Dynamic Neural Network Model of a Mirror System. Adaptive Behavior 12(2), 93–115 (2004)
Carpenter, G.A., Grossberg, S.: The ART of Adaptive Pattern Recognition by a Self-Organizing Neural Network. IEEE Computer, 77–886 (1988)
Lalanne, C., Lorenceau, J.: Crossmodal Integration for Perception and Action. J. Physiology, 265–279 (2004)
Carpenter, G.A., Grossberg, S.: ART 2: self-organization of stable category recognition codes for analog input patterns. Applied Optics 26(23), 4919–4930 (1987)
Breazeal, C., Scassellati, B.: Challenges in Building Robots that Imitate People. Imitation in Animals and Artifacts, 363–390 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, L., Su, J. (2006). Humanoid Robot Behavior Learning Based on ART Neural Network and Cross-Modality Learning. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_61
Download citation
DOI: https://doi.org/10.1007/11881070_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)