AI 中文总结
针对连续体机械臂刚性抓取精度不足的问题，提出支持增强型颗粒阻塞夹爪，结合强化学习控制器，实现模块化抓取任务，融合物理与具身智能。
AI 中文摘要
连续体机械臂在受限空间中能够提供灵巧的运动，但结构柔顺性、迟滞现象和负载相关的变形会留下残余的位置和方向误差，这些误差可能削弱与刚性夹爪的可靠接触。为解决这一局限，本文提出了一种专为连续体机械臂设计的轻量级支持增强型颗粒阻塞夹爪。该夹爪在阻塞前保持柔顺性，而在阻塞后建立起通向连续体机械臂末端的直接载荷路径。为提升其抓取性能，我们系统地设计了膜材料、颗粒、填充比率和内部支撑结构，并进一步识别了与接触偏移和物体形状相关的几何依赖抓取边界。基于这些结果，我们构建了一个物理操控系统，集成了连续体机械臂、颗粒阻塞夹爪、视觉反馈、腱驱动和气动控制。随后，我们在随机化仿真中训练了一个基于强化学习的到达控制器，并将其部署到物理系统上，展示了在模块化抓取-释放任务中定位控制与接触层面的机械适应如何互补。通过引入一种自适应结构，放宽了对高精度建模和定位控制的需求，这项工作探索了一种融合物理与具身智能的设计范式。
英文摘要
Continuum manipulators provide dexterous motion in confined spaces, but structural compliance, hysteresis, and load-dependent deformation leave residual position and orientation errors that can undermine reliable contact with rigid grippers. To address this limitation, this paper presents a lightweight support-enhanced granular-jamming gripper tailored to a continuum manipulator. The gripper maintains compliance before jamming while establishing a direct load path to the continuum manipulator tip after jamming. To improve its grasping performance, we systematically designed membrane materials, particles, filling ratios, and the internal support structure, and further identify geometry-dependent grasp boundaries with respect to contact offset and object shape. Building on these results, we construct a physical manipulation system integrating the continuum manipulator, granular-jamming gripper, visual feedback, tendon actuation, and pneumatic control. We then train a reinforcement-learning-based reaching controller in a randomized simulation and deploy it on the physical system, demonstrating how positioning control and contact level mechanical adaptation can complement each other in a modular grasp-and-release task. By introducing an adaptive structure that relaxes the need for highly accurate modeling and positioning control, this work explores a design paradigm that integrates physical and embodied intelligence.
Comments8 pages, 10 figures
