Dynamical systems for predictive control of autonomous robots |
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Authors: | J Michael Herrmann |
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Institution: | aMax-Planck-Institut Göttingen, Germany |
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Abstract: | Summary Regularities in the environment are accessible to an autonomous agents as reproducible relations between actions and perceptions
and can be exploited by unsupervised learning. Our approach is based on the possibility to perform and to verify predictions
about perceivable consequences of actions. It is implemented as a three-layer neural network that combines predictive perception,
internal-state transitions and action selection into a loop which closes via the environment. In addition to minimizing prediction
errors, the goal of network adaptation comprises also an optimization of the minimization rate such that new behaviors are
favored over already learned ones, which would result in a vanishing improvement of predictability. Previously learned behaviors
are reactivated or continued if triggering stimuli are available and an externally or otherwise given reward overcompensates
the decay of the learning rate. In the model, behavior learning and learning behavior are brought about by the same mechanism,
namely the drive to continuously experience learning success. Behavior learning comprises representation and storage of learned
behaviors and finally their inhibition such that a further exploration of the environment is possible. Learning behavior,
in contrast, detects the frontiers of the manifold of learned behaviors and provides estimates of the learnability of behaviors
leading outwards the field of expertise. The network module has been implemented in a Khepera miniature robot. We also consider
hierarchical architectures consisting of several modules in one agent as well as groups of several agents, which are controlled
by such networks. |
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Keywords: | autonomous robots predictive control place cells emergent behavior collective behavior |
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