DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative
  Diffusion Models
Tsun-Hsuan Wang Juntian Zheng Pingchuan Ma Yilun Du Byungchul Kim Andrew Spielberg Joshua Tenenbaum Chuang Gan Daniela Rus
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. DiffuseBot 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 at
https://diffusebot.github.io/