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Reservoir Computing in robotics: a review
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Reservoir Computing is a relatively new framework created to allow the usage of powerful but complex systems as computational mediums. The basic approach consists in training only a readout layer, exploiting the innate separation and transformation provided by the previous, untrained system. This approach has shown to possess great computational capabilities and is successfully used to achieve many tasks. This review aims to represent the current 'state-of-the-art' of the usage of Reservoir Computing techniques in the robotic field. An introductory description of the framework and its implementations is initially given. Subsequently, a summary of interesting applications, approaches, and solutions is presented and discussed. Considerations, ideas and possible future developments are proposed in the explanation.
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Cited by 2 Pith papers
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7-Methylquinolinium Iodobismuthate Memristor: Exploring Plasticity and Memristive Properties for Digit Classification in Physical Reservoir Computing
A lead-free bismuth-halide memristor is reported as a physical reservoir, with claimed MNIST accuracy of 82.26%, but the classification equations omit the reservoir outputs.
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Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis
DARC adds a reinforcement learning policy that modulates the context input of a fixed reservoir network, enabling a simulated robot arm to reach out-of-distribution targets and track a circle without retraining the reservoir.
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