Learning to control complex tensegrity robots

Tensegrity robots are based on the idea of tensegrity structures that provides many advantages critical to robotics such as being lightweight and impact tolerant. Unfortunately tensegrity robots are hard to control due to overall complexity. We use multiagent learning to learn controls of a ball-shaped tensegrity with 6 rods and 24 cables. Our simulation results show that multiagent learning can be used to learn an efficient rolling behavior and test its robustness to actuation noise.