基于扩散的不确定性感知优化的章鱼爬行多样化自适应臂部协调
Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization
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中文总结 AI 辅助
提出DUO算法，通过扩散模型学习章鱼爬行控制器，利用协调多样性实现软体多臂机器人的鲁棒适应，无需示范或重新训练。
中文摘要 AI 辅助
章鱼爬行启发了利用冗余性的软体机器人，但发现和组织多样化的协调模式以适应环境仍然具有挑战性。为解决这一问题，我们提出了一种基于扩散的不确定性感知优化（DUO）算法，该算法为模拟的、肌肉驱动的CyberOctopus学习无示范的爬行控制器。这项工作是将基于扩散的控制首次应用于接触丰富的模拟环境中的软体多臂机器人。通过将多种运动行为嵌入共享的控制分布中，该方法使模拟章鱼能够应对动态物理约束，表明学习到的协调多样性内在地促进了鲁棒适应。主要贡献包括：（i）一种对称结构的策略表示，将径向等效的控制器折叠到规范的方向扇区中；（ii）一种在线黑箱优化策略，即DUO算法，用于发现并保留多样的协调模式；（iii）一种控制编辑技术，可在不重新训练的情况下将现有控制器适应新的执行器约束。这些结果表明，学习到的协调多样性使运动冗余成为软体多臂机器人适应中的实用资源。
英文摘要
Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging. To address this, we introduce a Diffusion-based Uncertainty-aware Optimization (DUO) algorithm that learns demonstration-free crawling controllers for a simulated, muscle-actuated CyberOctopus. This work represents the first application of diffusion-based control to soft multi-arm robots in contact-rich simulations. By embedding a variety of locomotion behaviors within a shared control distribution, this approach enables the simulated octopus to navigate dynamic physical constraints, demonstrating that learned coordination diversity inherently facilitates robust adaptation. The main contributions include: (i) a symmetry-structured policy representation that folds radially equivalent controllers into a canonical directional sector, (ii) an online black-box optimization strategy, the DUO algorithm, that discovers and retains diverse coordination modes, and (iii) a control editing technique that adapts existing controllers to novel actuator constraints without retraining. These results show how learned coordination diversity makes motor abundance a practical resource for adaptation in soft multi-arm robots.
发表机构
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
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