Tensegrity robots are composed of rigid rods connected by elastic cables, and their unique light-weight yet compliant structure makes them an appealing choice for space exploration. However, locomotion control for these robotic systems remains difficult due to their nonlinear dynamics and high-dimensional state space. We demonstrate that in the domain of tensegrity robotics, it is possible to efficiently learn end-to-end locomotion policies using mirror descent guided policy search (MDGPS) even with limited sensory inputs. We compare learned neural network policies with other locomotion control policies in various testing environments; and results show that neural network policies consistently outperform others. We also shed light to the policy learning process by analyzing different choices of observation inputs to the robot. Moreover these findings motivate exploration of deep reinforcement learning algorithms in the domain of tensegrity robotics. We show preliminary results with one such locomotion example on discontinuous rough terrains.

This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme centered around the simultaneous actuation of multiple cables, the robot demonstrates robust climbing on inclined surfaces in hardware experiments and speeds significantly faster than previous spherical tensegrity models. This robot is an improvement over other iterations in the TT-series and the first tensegrity to achieve reliable locomotion on inclined surfaces of up to 24°. We analyze locomotion in simulation and hardware under single and multi-cable actuation, and introduce two novel multi-cable actuation policies, suited for steep incline climbing and speed, respectively. We propose compelling justifications for the increased dynamic ability of the robot and motivate development of optimization algorithms able to take advantage of the robot's increased control authority.