arXiv:2510.27666cs.RO2025-10被引 1

通过全身自感知变形,实现跨尺度抓取的模块化软夹爪。

Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping

  • 采用分布式自感气动模块,可全局重构形态。
  • 抓取范围扩大至10倍,适配不规则物体与多尺度目标。
  • 适合需要高灵巧性的机器人抓取场景。

生物系统如章鱼能通过整体形态自适应重构实现跨尺度操作,这一能力在机器人领域仍难以实现。传统软夹爪虽具柔顺性,但受限于固定全局形态;以往的形态变化也仅限局部变形,无法复现这种生物灵巧性。受此启发,我们提出协同式全身自感知变形新范式,并构建模块化软夹爪架构。该设计由分布式自感气动执行器网络组成,能智能重构整体拓扑,实现多种可控的多边形形态。通过嵌入式传感器获取丰富本体感觉反馈,系统可无缝切换从精细捏握到大范围包裹抓取。实验表明,该方法显著扩展抓取范围,提升对多样化几何形状(标准与非规则)和尺度(最高达10倍)的泛化能力,并解锁多对象及内部钩状抓取等新型操作模式。本工作提供一种低成本、易制造、可扩展的集成驱动与传感框架,为实现机器人操作中的生物级灵巧性开辟新路径。

原文摘要 · Abstract (English)

Biological systems, such as the octopus, exhibit masterful cross-scale manipulation by adaptively reconfiguring their entire form, a capability that remains elusive in robotics. Conventional soft grippers, while compliant, are mostly constrained by a fixed global morphology, and prior shape-morphing efforts have been largely confined to localized deformations, failing to replicate this biological dexterity. Inspired by this natural exemplar, we introduce the paradigm of collaborative, whole-body proprioceptive morphing, realized in a modular soft gripper architecture. Our design is a distributed network of modular self-sensing pneumatic actuators that enables the gripper to intelligently reconfigure its entire topology, achieving multiple morphing states that are controllable to form diverse polygonal shapes. By integrating rich proprioceptive feedback from embedded sensors, our system can seamlessly transition from a precise pinch to a large envelope grasp. We experimentally demonstrate that this approach expands the grasping envelope and enhances generalization across diverse object geometries (standard and irregular) and scales (up to 10$\times$), while also unlocking novel manipulation modalities such as multi-object and internal hook grasping. This work presents a low-cost, easy-to-fabricate, and scalable framework that fuses distributed actuation with integrated sensing, offering a new pathway toward achieving biological levels of dexterity in robotic manipulation.

软体机器人灵巧抓取自感知

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。