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 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 article 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 to generate a wealth of dynamical behaviors.
Highlights
Unlike machines, animals exhibit a tremendous amount of resilience, due, in part, to their intertwining of soft tissues and rigid skeletons
We describe a new class of soft robot based upon a tensegrity structure driven by vibration
We investigate our hypothesis that the interplay between a flexible tensegrity structure and vibration is the key for effective locomotion
Summary
Animals exhibit a tremendous amount of resilience, due, in part, to their intertwining of soft tissues and rigid skeletons. Taking inspiration from the natural world, the field of soft robotics seeks to address some of the constraints of conventional rigid robots through the use of compliant, flexible, and elastic materials.[4,5] Trimmer et al, for instance, construct soft robots from silicone rubber, using shape memory alloy microcoil actuation, which can slowly crawl in a controlled manner[6] or roll in an uncontrolled ballistic manner.[7] research by Whitesides et al uses pneumatic inflation to produce slow, dynamically stable crawling motions[8] as well as fast, but less controlled tentacle-like grippers,[9] combustiondriven jumpers[10] and a self-contained microfluidic ‘‘octobot.’’5. To find the right vibrational frequencies, we equipped the robot with a data-efficient trial-and-error algorithm, which allows it to adapt when needed
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