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，我们将无源性控制（PBC）与强化学习（RL）技术相结合，旨在消除彼此弱点，并在所生成的框架中激发具有前景的新特性。我们将贡献置于通过虚拟能量储层实现PBC的场景中——这是一种旨在对任意控制输入实现闭环无源性的控制技术。尽管该成果应用广泛，但我们讨论了其当前阶段实际应用仍相当有限的原因，这与“无源性技术会导致性能损失”这一备受争议的说法相呼应。利用强化学习，我们可以学习一种控制策略，该策略通过能量储层架构实现被动化，从而融合了学习方法的灵活性与可基于能量储层推断的系统理论属性。仿真结果验证了该方法的有效性，并揭示了能源感知机器人学中新颖且有趣的研究方向。