Adaptive and Resilient Soft Tensegrity Robots

TL;DR

This paper introduces a soft tensegrity robot that uses machine learning to discover effective locomotion gaits, demonstrating resilience and adaptability similar to biological organisms, with minimal physical trials needed.

Contribution

It presents an easy-to-assemble soft tensegrity robot that employs machine learning for gait discovery, enhancing resilience and dynamic locomotion capabilities.

Findings

Robot achieves highly dynamic locomotive gaits

Demonstrates structural and behavioral resilience to damage

Machine learning reduces physical trial requirements

Abstract

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties in order to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control -- and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This manuscript describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics in order to generate a wealth of dynamical behaviors.