发表机构
School of Computer Science, Shanghai Jiao Tong University; Intelligent Game and Decision Laboratory; School of Statistics, Renmin University of China; School of Artificial Intelligence, Shanghai Jiao Tong University; Shanghai Innovation Institute; Center for Applied Statistics, Renmin University of China（上海交通大学计算机科学学院; 智能博弈与决策实验室; 中国人民大学统计学院; 上海交通大学人工智能学院; 上海创新研究院; 中国人民大学应用统计中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RoboLDA通过贝叶斯概率模型将体素软机器人形态生成分解为任务-机器人-器官-体素四层层次，利用变分推断提取设计先验，实现零样本设计，性能达进化算法的106.4%。
AI 中文摘要
机器人学的最新进展凸显了机器人形态的层次化配置，其中多个层次的功能子结构协同作用以促进智能行为。这种层次化视角虽然特别有利于体素软机器人（VSRs）以简化设计和控制复杂性，但其高度依赖领域专业知识而受到阻碍。在这项工作中，我们解决以下问题：我们能否仅从现有的成功设计中推导出这种层次化设计原则？我们通过提出RoboLDA来肯定地回答，这是一种贝叶斯概率模型，将VSR形态生成分解为四个层次：“任务-机器人-器官-体素”，并通过变分推断进行训练。通过对模拟VSRs的大量实验，我们验证了高性能VSR设计背后存在一致、直观的层次化模式，并展示了RoboLDA提取和利用这些层次化先验以在未见任务中进行零样本机器人设计的能力。即使没有进一步优化，生成的设平均达到进化算法产生的优化性能的106.4%。此外，RoboLDA推断出的器官结构作为有效的功能子结构，在与模块化控制策略集成时显著增强了协同运动控制。我们的工作开创了机器人形态的层次化生成建模，为更具可解释性和泛化性的具身智能体开发提供了一条有前景的途径。
英文摘要
Recent advances in robotics highlight hierarchical configurations of robot morphology, where multiple levels of functional substructures synergize to facilitate intelligent behaviors. This hierarchical perspective, while particularly advantageous for voxel-based soft robots (VSRs) to ease design and control complexities, is hindered by its heavy reliance on domain expertise. In this work, we address the following question: can we derive such hierarchical design principles solely from existing successful designs? We answer affirmatively by presenting RoboLDA, a Bayesian probabilistic model that decomposes VSR morphology generation into a four-level hierarchy: "task-robot-organ-voxel", and is trained via variational inference. Through extensive experiments on simulated VSRs, we verify the presence of consistent, intuitive hierarchical patterns underlying high-performing VSR designs and showcase RoboLDA's proficiency to extract and leverage these hierarchical priors for zero-shot robot design in unseen tasks. The generated designs, even without further optimization, achieve on average 106.4% of the optimized performance produced by evolutionary algorithms. Additionally, the organ structures inferred by RoboLDA serve as valid functional substructures, significantly enhancing synergistic motion control when integrated with modular control policies. Our work pioneers hierarchical generative modeling of robot morphology, offering a promising pathway towards more interpretable and generalizable development of embodied agents.
