发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种自包含的模块化连续体机器人平台，结合可重构关节与磁传感器及模块化学习实现原位自位姿估计，无需外部跟踪，实验验证了其自感知与适应能力。
AI 中文摘要
连续体机器人在受限环境中能够实现平滑的形状变形和安全交互。然而，现有的大多数系统是任务特定的，并依赖外部传感基础设施，这限制了它们的适应性和实际部署。本文提出了一种自包含的模块化连续体机器人平台，将机械可重构性与机载位姿估计相结合。该机器人由可互换的连续体关节构成，关节刚度经解析预计算，可实现快速组装和机器人形状的直接编程。本体感觉通过磁传感器和基于模块化的学习框架实现，其中每个关节训练一个单一模型，并在不同配置间复用。该系统在真实世界中进行了实验验证，展示了无需外部跟踪的自感知能力和适应性。
英文摘要
Continuum robots enable smooth shape morphing and safe interaction in confined environments. However, most existing systems are task-specific and depend on external sensing infrastructure, limiting their adaptability and real-world deployment. This paper presents a self-contained modular continuum robotic platform that combines mechanical reconfigurability with onboard pose estimation. The robot is constructed from interchangeable continuum joints with analytically precomputed stiffness, allowing rapid assembly and direct programming of the robot shape. Proprioceptive sensing is achieved using magnetic sensors and a modular learning-based framework, where a single model is trained per joint and reused across configurations. The system is experimentally validated in real world, demonstrating self-sensing capabilities and adaptation without external tracking.
