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A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control

David Navarro-Alarcon 1 Jiaming Qi 1 Jihong Zhu 1 Andrea Cherubini 2, 1
2 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialize in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.
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David Navarro-Alarcon, Jiaming Qi, Jihong Zhu, Andrea Cherubini. A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control. Frontiers in Neurorobotics, Frontiers, 2020, 14, pp.#59. ⟨10.3389/fnbot.2020.00059⟩. ⟨lirmm-02985680⟩

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