发表机构
Shanghai Jiao Tong University; Shanghai Innovation Institute; Renmin University of China; Intelligent Game and Decision Laboratory（上海交通大学; 上海创新研究院; 中国人民大学; 智能博弈与决策实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MISCO框架，结合深度生成模型与分布估计算法，用于体素软体机器人设计优化，具备理论最优性保证和高效采样能力，实验验证其在多样化任务中的有效性。
AI 中文摘要
体素软体机器人（VSRs）为开发具有类人智能的人工生物体提供了一条有前景的途径。然而，巨大的设计空间和昂贵的评估对其设计优化构成了重大挑战。在此，我们开发了MISCO，一种由深度生成模型赋能的新型进化框架，用于在具有理论保证的情况下优化VSR设计。MISCO将分布估计算法与精心设计的变分自编码器相结合，该自编码器具备多任务学习、位置感知和体素间信号传递功能。这些关键组件增强了VSR形态的表征能力，并促进了形态分布的高效采样和优化。我们为MISCO渐近收敛到全局最优设计提供了理论保证，并给出了有利的收敛速度。大量模拟实验进一步证明了MISCO在导航广阔设计空间方面的卓越有效性，能够为多样化任务进化出高性能VSR，同时灵活平衡优化效率和形态多样性。经过经验和理论的双重验证，MISCO代表了向更可扩展和更可靠的软体机器人开发迈出的重要一步。
英文摘要
Voxel-based soft robots (VSRs) present a promising avenue for developing artificial organisms with lifelike intelligence. However, the vast design spaces and expensive evaluations substantially challenge their design optimization. Here we develop MISCO, a novel evolutionary framework empowered by deep generative models to optimize VSR designs with theoretical guarantees. MISCO integrates an estimation-of-distribution algorithm with a meticulously designed variational autoencoder featuring multi-task learning, position awareness, and inter-voxel signaling. These key components enhance the representational capacity of VSR morphologies and facilitate highly efficient sampling and optimization of morphological distributions. We provide theoretical guarantees for MISCO's asymptotic convergence to globally optimal designs, alongside a favorable convergence rate. Extensive simulated experiments further demonstrate MISCO's exceptional effectiveness in navigating vast design spaces, evolving high-performing VSRs for diverse tasks while flexibly balancing optimization efficiency and morphological diversity. Being validated both empirically and theoretically, MISCO represents a step change towards more scalable and reliable soft robot development.
