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 to a loss of performance. The use of RL allows to learn a control policy which 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，我们融合了基于被动性的控制与强化学习技术，旨在消除两者各自的弱点，并在所构建的框架中引入新的有前景特性。我们的贡献聚焦于以下场景：通过虚拟能量罐实现基于被动性的控制——该控制技术旨在对任意控制输入实现闭环被动性。尽管后者的结果被广泛应用，我们仍讨论了其在当前阶段实际应用仍相当受限的原因，这与"基于被动性的技术会导致性能损失"这一争议性论断相呼应。强化学习可用于学习能通过能量罐架构被动化的控制策略，从而结合了学习方法的通用性与能量罐所推断出的系统理论特性。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。