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/

翻译：自然界演化出具有高度形态和行为智能复杂性的生物，而计算方法在接近这种多样性和效能方面仍显滞后。人工生物形态与控制的协同优化在虚拟环境中展现出应用于物理软体机器人和虚拟角色创建的潜力；然而，此类方法需要开发能够基于纯结构推理功能的新型学习算法。本文提出DiffuseBot——一种物理增强扩散模型，可生成能够在广泛任务中表现出色的软体机器人形态。DiffuseBot通过以下方式弥合虚拟生成内容与物理实用性之间的鸿沟：(i) 用物理动力学仿真增强扩散过程，提供性能证明；(ii) 引入协同设计流程，利用可微仿真中的物理敏感性信息，联合优化物理设计与控制。我们展示了一系列仿真与制造的机器人及其能力。详情请访问我们的网站：https://diffusebot.github.io/