Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms.

Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem.

Design and control of compliant tensegrity robots through simulation and hardware validation.

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center, Moffett Field, CA, USA, has developed and validated two software environments for the analysis, simulation and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ('tensile-integrity') structures have unique physical properties that make them ideal for interaction with uncertain environments.