Abstract
We propose a new neuro-robotics network architecture that can generate goal-oriented behavior for visually-guided multiple object manipulation task by a humanoid robot. For examples, given a “sequential hit” multiple objects task, the proposed network is able to modulate a humanoid robot’s behavior by taking advantage of suitable timing for gazing, approaching and hitting the object and again for the other object. To solve a multiple object manipulation task via learning by examples, the current study considers two important mechanisms: (1) stereo visual attention with depth estimation for movement generation, dynamic neural networks for behavior generation and (2) their adaptive coordination. Stereo visual attention provides a goal-directed shift sequence in a visual scan path, and it can guide the generation of a behavior plan considering depth information for robot movement. The proposed model can simultaneously generate the corresponding sequences of goal-directed visual attention shifts and robot movement timing with regards to the current sensory states including visual stimuli and body postures. The experiments show that the proposed network can solve a multiple object manipulation task through learning, by which some novel behaviors without prior learning can be successfully generated.
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
Johansson, R.S., Westling, G., Bäckström, A.: Eye-hand coordination in object manipulation. The Journal of Neuroscience 21, 6917–6932 (2001)
Ballard, D.H., Hayhoe, M.M., Li, F., Whitehead, S.D., Frisby, J.P., Taylor, J.G., Fisher, R.B.: Hand-eye coordination during sequential tasks [and Discussion]. Philosophical Transactions: Biological Sciences Royal Society 337, 331–339 (1992)
Berkinblit, M.B., Fookson, O.I., Smetanin, B., Adamovich, S.V., Poizner, H.: The interaction of visual and proprioceptive inputs in pointing to actual and remembered targets. Exp. Brain Res. 107, 326–330 (1995)
Jeong, S., Ban, S.-W., Lee, M.: Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment. Neural Networks 21, 1420–1430 (2008)
Jeong, S., Lee, M., Arie, H., Tani, J.: Developmental learning of integrating visual attention shifts and bimanual object grasping and manipulation tasks. In: Proc. IEEE Int. Conf. Develop. Learn., Ann Arbor, MI, pp. 165–170 (2010)
Choi, S.B., Jung, B.S., Ban, S.W., Niitsuma, H., Lee, M.: Biologically motivated vergence control system using human-like selective attention model. Neurocomputing 69, 537–558 (2006)
Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Comput. Biol. 4(11), 1–18 (2008)
Nishimoto, R., Tani, J.: Development of hierarchical structures for actions and motor imagery: a constructivist view from synthetic neurorobotics study. Psychological Research 73, 545–558 (2009)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1(2), 270–280 (1989)
Doya, K., Yoshizawa, S.: Memorizing oscillatory patterns in the analog neuron network. In: Proceedings of 1989 International Joint Conference on Neural Networks, Washington, D.C, vol. I, pp. 27–32 (1989)
Saarinen, J., Kohonen, T.: Self-organized formation of colour maps in a model cortex. Perception 14(6), 711–719 (1985)
Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeong, S., Park, Y., Arie, H., Tani, J., Lee, M. (2011). Goal-Oriented Behavior Generation for Visually-Guided Manipulation Task. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-24955-6_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
Online ISBN: 978-3-642-24955-6
eBook Packages: Computer ScienceComputer Science (R0)