Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search

6 Apr 2020 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... (read more)

PDF Abstract



  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

Random Search
Hyperparameter Search