Ground Reaction Force Estimation in a Quadruped Robot via Liquid State Networks

This paper aims to investigate the Liquid State Machines (LSMs) learning capability and robustness of a complex robot-environment interaction. The goal is to design an efficient robot state estimation method based on reservoir computing. The method maps local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot. The robot taken into account is the simulated version of Lilibot, a small-sized and reconfigurable bio-inspired robot with multiple real-time sensory feedback. Global information was provided from the ground reaction forces acquired on the tips of each leg. Simulation results are reported and compared, also in presence of faulty conditions in the sensory system.