Online Learning of Visuo-Motor Coordination in a Humanoid Robot: A Biologically Inspired Model

G. Schillaci, V. V. Hafner (Humboldt Universität zu Berlin) and B. Lara (Universidad Autónoma del Estado de Morelos)
4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Genova, Italy, October 13 – 16, 2014.

[showhide type=”Abstract”]Abstract: Coordinating vision with movements of the body is a fundamental prerequisite for the development of complex motor and cognitive skills. Visuo-motor coordination seems to rely on processes that map spatial vision onto patterns of muscular contraction. In this paper, we investigate the formation and the coupling of sensory maps in the humanoid robot Aldebaran Nao. We propose a biologically inspired model for coding internal representations of sensorimotor experience that can be fed with data coming from different motor and sensory modalities, such as visual, auditory and tactile. The model is inspired by the self-organising properties of areas in the human brain, whose topologies are structured by the information produced through the interaction of the individual with the external world. In particular, Dynamic Self-Organising Maps (DSOMs) proposed by Rougier et al. [1] have been adopted together with a Hebbian paradigm for on-line and continuous learning on both static and dynamic data distributions. Results show how the humanoid robot improves the quality of its visuo-motor coordination over time, starting from an initial configuration where no knowledge about how to visually follow its arm movements is present. Moreover, plasticity of the proposed model is tested. At a certain point during the developmental timeline, a damage in the system is simulated by adding a perturbation to the motor command used for training the model. Consequently, the performance of the visuo-motor coordination is affected by an initial degradation, followed by a new improvement as the proposed model adapts to the new mapping.[/showhide]

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Paper: Paper_ICDL-EpiRob2014_UBER