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.

翻译：在机器人学背景下，我们将基于无源性的控制（PBC）技术与强化学习（RL）方法相融合，旨在消除两者各自的部分缺陷，同时为最终框架赋予新的有前景特性。本研究立足于PBC通过虚拟能量罐实现的场景——该控制技术旨在为任意控制输入建立闭环无源性。尽管后者成果被广泛使用，但我们论证了其当前阶段的实际应用仍相当有限，这触及了“无源性技术常伴随性能损失”这一备受争议的观点。采用强化学习使我们能够学习到一种可通过能量罐架构实现被动化的控制策略，从而结合了学习方法的灵活性与因能量罐而可推断的系统理论特性。仿真实验验证了该方法的有效性，并揭示了能量感知机器人学领域若干新颖且值得关注的研究方向。