Abstract
Regardless of complex, unknown, and dynamically-changing environments, living creatures can recognize situated environments and behave adaptively in real-time. However, it is impossible to prepare optimal motion trajectories with respect to every possible situations in advance. The key concept for realizing the environment cognition and motor adaptation is a context-based elicitation of constraints which are canalizing well-suited sensorimotor coordination. For this aim, in this study, we propose a polymorphic neural networks model called CTRNN+NM (CTRNN with neuromodulatory bias). The proposed model is applied to two dimensional arm-reaching movement control under various viscous force fields. The parameters of the networks are optimized using genetic algorithms. Simulation results indicate that the proposed model inherits high robustness even though it is situated in unexperienced environments.
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Kondo, T., Ito, K. (2007). An Intrinsic Neuromodulation Model for Realizing Anticipatory Behavior in Reaching Movement under Unexperienced Force Fields. In: Butz, M.V., Sigaud, O., Pezzulo, G., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2006. Lecture Notes in Computer Science(), vol 4520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74262-3_14
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DOI: https://doi.org/10.1007/978-3-540-74262-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74261-6
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