Papers › Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search
Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search
Vladislav Kurenkov, Hany Hamed, Sergei Savin
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.
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