Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Reinforcement learning in motor control
The handbook of brain theory and neural networks
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?
Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Biological arm motion through reinforcement learning
Biological Cybernetics
Reinforcement Learning in Continuous Time and Space
Neural Computation
Feed-forward control of a redundant motor system
Biological Cybernetics
Neural network approaches to dynamic collision-free trajectorygeneration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The ability to learn from interaction with the exterior world as well as variability are two main features of living organisms. The aim of this study is to present and discuss the property of a stochastic reinforcement learning based model of upper limb posture generation that exhibits both properties. The capacity of the model to discover suitable postures satisfying task and obstacle avoidance constraints is demonstrated by simulation. Also, task equivalent configurations that can be linked to recent findings in the motor control literature are generated by the proposed formalism due to its stochastic nature.