Abstract:

The co-design paradigm claims substantial advantages to hardware and control system design by addressing multidisciplinary challenges within a unified framework. Es- tablished co-design frameworks for robot manipulators have predominantly focused on two components: motor/arm design and trajectory optimization, which inadequately address real- world disturbances and model uncertainties and thus render sub- optimal design and closed-loop system performance. This paper proposes a closed-loop co-design (CLCD) framework to jointly determine motors, motions, and a feedback controller, where the controller comprises a reinforcement learning (RL)-based compensator and a classic proportional-derivative controller for trajectory tracking. Simulation is performed to validate 1) the effectiveness of the proposed CLCD framework to attenuate the sim-2-real gap, 2) the viability of incorporating an RL-based controller into the CLCD for flexible and efficient synthesis of control policy, and 3) the scalability of the CLCD by applying it to perform co-design for 12 and 120 tasks.