2023/01/30 by Riccardo Zanella, Zanella, Riccardo, Gianluca Palli +5
Engineering · Medicine · Neuroscience · #FOS: Computer and information sciences #FOS: Electrical engineering #Muscle activation and electromyography studies #Neurological disorders and treatments #Neuroscience and Neural Engineering #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2301.12759
openalex publication_date 2023/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Within a robotic context, we merge the techniques of passivity-based control (PBC) and reinforcement learning (RL) with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. We frame our contribution in a scenario where PBC is implemented by means of virtual energy tanks, a control technique developed to achieve closed-loop passivity for any arbitrary control input. Albeit the latter result is heavily used, we discuss why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity-based techniques are associated with a loss of performance. The use of RL allows us to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy-aware robotics.