论文标题
通过增强随机搜索学习稳定控制政策，以实现紧张的料斗
Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search
论文作者
论文摘要
在本文中，我们考虑了紧张的霍珀 - 一种基于张力的新型机器人，能够通过跳跃而移动。本文着重于稳定控制策略的设计，这些策略是通过增强的随机搜索方法获得的。特别是，我们搜索控制策略，使料斗在执行一次跳跃后保持垂直稳定性。可以证明，料斗可以维持垂直配置，但要遵守不同的初始条件并随着控制频率的变化。特别是，将控制频率从训练中的1000Hz降低到执行中的500Hz并不影响平衡任务的成功率。
In this paper, we consider tensegrity hopper - a novel tensegrity-based robot, capable of moving by hopping. The paper focuses on the design of the stabilizing control policies, which are obtained with Augmented Random Search method. In particular, we search for control policies which allow the hopper to maintain vertical stability after performing a single jump. It is demonstrated, that the hopper can maintain a vertical configuration, subject to the different initial conditions and with changing control frequency rates. In particular, lowering control frequency from 1000Hz in training to 500Hz in execution did not affect the success rate of the balancing task.
