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arXiv · 2009.10864

Behavioral Repertoires for Soft Tensegrity Robots

Kyle Doney·Aikaterini Petridou·Jacob Karaul·Ali Khan·Geoffrey Liu·John Rieffel
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Abstract

Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counterintuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. A second challenge is that soft materials are difficult to simulate with high fidelity -- leading to a significant reality gap when trying to discover or optimize new behaviors. In this work we employ a Quality Diversity Algorithm running model-free on a physical soft tensegrity robot that autonomously generates a behavioral repertoire with no a priori knowledge of the robot dynamics, and minimal human intervention. The resulting behavior repertoire displays a diversity of unique locomotive gaits useful for a variety of tasks. These results help provide a road map for increasing the behavioral capabilities of mobile soft robots through real-world automation.

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Kyle Doney, Aikaterini Petridou, Jacob Karaul, Ali Khan, Geoffrey Liu, John Rieffel. 2020-09-23. Behavioral Repertoires for Soft Tensegrity Robots. https://arxiv.org/abs/2009.10864

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