DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models

Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-optimization of artificial creatures' morphology and control in silico shows promise for applications in physical soft robotics and virtual character creation; such approaches, however, require developing new learning algorithms that can reason about function atop pure structure. In this paper, we present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. \name bridges the gap between virtually generated content and physical utility by (i) augmenting the diffusion process with a physical dynamical simulation which provides a certificate of performance, and (ii) introducing a co-design procedure that jointly optimizes physical design and control by leveraging information about physical sensitivities from differentiable simulation. We showcase a range of simulated and fabricated robots along with their capabilities. Check our website: https: //diffusebot. github. io/

Authors

• Tsun-Hsuan Johnson Wang
• Juntian Zheng
• Pingchuan Ma CompVis @ LMU Munich, Munich Center for Machine Learning
• Yilun Du
• Byungchul Kim
• Andrew Spielberg
• Josh Tenenbaum
• Chuang Gan University of Massachusetts at Amherst
• Daniela Rus Massachusetts Institute of Technology

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