Trajectory Optimization Framework for Rehabilitation Robots With Multi-Workspace Objectives and Constraints

Robot-assisted neurorehabilitation requires trajectories between arbitrary poses in the patient's range of motion. Data-driven optimization methods, such as Learning by Demonstration, are well suited to replicate complex multi-joint movements. However, these methods lack individualization to patient-, robot- and exercise-specific constraints. We propose a hybrid optimization framework that combines cost-based objectives, such as minimizing jerk, with the data-driven optimization of a reference trajectory. The objectives can be individually weighted in a sequential quadratic program with application-related constraints represented in intuitive workspaces. We demonstrated that trajectories recorded from an existing upper-limb activity dataset could be adapted to the personal needs of a healthy participant with simulated impairments, the hardware-specific robot topology, and changes in the exercise setup. Furthermore, we showed how redundancies in the degrees of freedom of the arm can be exploited: For example, an elbow angle movement of 30.4
${^\circ }$
was compensated entirely through increased wrist movement in a reach-goal task. In addition to making sequential quadratic programming more accessible to the field of rehabilitation robotics, our framework improves the variability and individualizability of generated trajectories for patients, provides more adaptation possibilities to the therapist, and enables sharing of recorded movement data between robotic platforms, patients, and exercises.