title: Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots.
abstract: Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.
authors.affiliation: Institute for Physics - Biophysics, George-August-University Göttingen, Germany ; Bernstein Center for Computational Neuroscience, George-August-University Göttingen, Germany ; Laboratory for Neural Computation and Adaptation, Riken Brain Science Institute Saitama, Japan.
authors.forename: Sakyasingha
authors.initials: S
authors.lastname: Dasgupta
authors.affiliation: Bernstein Center for Computational Neuroscience, George-August-University Göttingen, Germany.
authors.forename: Dennis
authors.initials: D
authors.lastname: Goldschmidt
authors.affiliation: Institute for Physics - Biophysics, George-August-University Göttingen, Germany ; Bernstein Center for Computational Neuroscience, George-August-University Göttingen, Germany.
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
authors.affiliation: Bernstein Center for Computational Neuroscience, George-August-University Göttingen, Germany ; CBR, Embodied AI and Neurorobotics Lab, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark Odense, Denmark.
authors.forename: Poramate
authors.initials: P
authors.lastname: Manoonpong
keywords: adaptive behavior; forward models; locomotion; neural control; recurrent networks; synaptic adaptation; walking robots