A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control
This addresses sensor-based control for robots, offering a stable and data-efficient solution, though it appears incremental as it builds on existing adaptive methods.
The paper tackles the problem of approximating unknown sensorimotor models for robot control using only feedback signals, achieving a method that requires minimal data and ensures Lyapunov stability.
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 specialise 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.