Adaptive robot control using modular reservoir computing to minimize multimodal errors

Adaptive kinematic/dynamic control of a robot arm (manipulator) is important for the robust execution of various tasks, including target tracking, when changes in the environment and robot’s physical parameters, such as mass and friction, occur. For highly accurate model-free adaptive tracking control, we propose an error-correction model using the reservoir of basal dynamics (reBASICS) computing method. reBASICS is trained to minimize multimodal errors, such as those in the end-effector position (visually detected) and torques (proprioceptively detected), and to correct robot trajectories. If errors are corrected simultaneously, the corrections can interfere with each other. Therefore, the model initially corrects torques to stabilize robot movements, followed by position correction. In the simulation of a two-link robot arm, approximate inverse kinematics (IK) and proportional-derivative (PD) controllers were assumed to produce tracking errors. The results showed that reducing errors based on reBASICS produced smaller tracking errors from the reference trajectory compared to that using a conventional echo state network (ESN). Furthermore, reducing both position and torque errors resulted in better performance than reducing position-only and torque-only errors.