Imitation Learning-Based System for the Execution of Self-Paced Robotic-Assisted Passive Rehabilitation Exercises

Abstract

The development of robotic-assisted rehabilitation exercises involving physical human-robot interaction requires extreme care since an injured limb may be in physical contact with the robot, so compliant behavior is imperative for these tasks. Typical approaches involve force control schemes like admittance controllers that allow humans to adapt the motion. However, when the patient's limb has limited mobility or is potentially injured, unintentional forces may occur during the robot's trajectory that could be incompatible with these controllers. This paper addresses a new way of generating compliant trajectories for passive rehabilitation exercises, considering that previous positions of the trajectory are attainable for the patient, so reversing the trajectory is a safe operation. Since there is no clear way to optimize such a goal due to the physiological variability among patients, the condition of reversal is based on imitation learning by taking the analogous healthy limb of the patient as a reference and encoding the forces using Gaussian Mixture Regression, and reversibility is accomplished by means of Reversible Dynamic Movement Primitives. The system allows for self-paced rehabilitation exercises by back-and-forth movements along the trajectory according to the patient's reaction, and it has been successfully applied to a 4-DOF parallel robot for lower-limb rehabilitation.