DiffuseBot: 物理拡張拡散モデルによるソフトロボットの創成
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
物理シミュレーションを組み込んだ拡散モデルでソフトロボットの形状を生成し、微分可能シミュレーションによる制御との共同設計で多様なタスクをこなせるロボットを自動生成する手法を提案。
著者: Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Andrew Spielberg, Joshua Tenenbaum, Chuang Gan, Daniela Rus
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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/
