Enhancement of the Robustness of Redundant Robot Arms Against Perturbations by Inferring Dynamical Systems Using Echo State Networks

Hiroshi Atsuta, Yuji Kawai, Minoru Asada

Abstract: A critical aspect in robot technology lies in ensuring that the robot arm executes the intended task or movement. The conventional teaching–playback technique for programming robot arm movements generates excessive torque when the initial posture differs from that during motion teaching or when external disturbances disrupt robotic arm execution. To address this problem, this study proposed a motion generation system with echo state networks (ESNs) for robot arms. These networks can reproduce trajectories including features such as attractors and bifurcations by training time-series data originating from a dynamical system. By leveraging this capability, the proposed approach enables an ESN to infer a dynamical system underlying demonstrated motions to perform a specific task on the basis of state feedback. Subsequently, the trained ESN generates reference signals to replicate the learned motion based on the current robot state. These reference signals can be used for linear feedback at the joint level. Redundant robots can easily transition to unobserved states because of their degrees of freedom. However, the trained ESN exhibits remarkable generalization capabilities, effectively guiding the robot back to its intended motion in those unobserved states. Numerical simulations confirmed that the proposed system dynamically adapts to robot behavior, effectively mitigating excessive acceleration, even in scenarios with initial posture deviations or external disturbances, thereby outperforming conventional teaching–playback methods.