在机器人学中利用虚拟能量池学习被动策略
Learning passive policies with virtual energy tanks in robotics
AI总结：
本文提出将虚拟能量池与强化学习相结合，学习可被无源化的机器人控制策略，以克服PBC性能损失问题并兼顾学习灵活性与系统稳定性。
摘要：
在机器人学背景下，我们融合了基于无源性的控制（PBC）与强化学习（RL）技术，旨在消除二者各自的一些弱点，并在所得框架中引入新的有前景的特性。我们将贡献设定在如下场景中：PBC通过虚拟能量池实现，这是一种为任意控制输入实现闭环无源性而开发的控制技术。尽管后者这一结果被大量使用，我们仍讨论了为何其在当前阶段的实际应用仍然相当有限，这与备受争议的“基于无源性的技术会带来性能损失”这一主张相呼应。RL的使用使我们能够学习一种可利用能量池架构进行无源化的控制策略，从而结合了学习方法的灵活性与因能量池而可推断的系统理论性质。仿真结果验证了该方法的有效性，并展示了能量感知机器人学中新颖有趣的研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
