arXiv:2505.18994cs.RO2025-05被引 4

通过可调针脚设计实现自适应抓取,提升机械手对未知物体的泛化能力。

Designing Pin-pression Gripper and Learning its Dexterous Grasping with Online In-hand Adjustment

  • 每个手指配备可独立伸缩的针脚阵列,实时调整形状贴合物体
  • 在手中动态调整针脚,实现物体姿态重定位,增强抓取稳定性
  • 结合强化学习与课程学习,实现在真实机器人上的高效抓举

本文提出一种受针压玩具启发的新型平行爪夹持器,其独特之处在于每个手指集成二维针脚阵列,可独立伸缩。该设计使夹持器能即时调整手指形态以贴合被夹物体,实现自适应抓取。同时,通过动态调节针脚,夹持器可在手中完成物体姿态重定位,显著提升抓取稳定性。为学习该夹持器的动态抓取技能,我们设计了专有的强化学习算法,包括状态表示与奖励函数优化,并引入课程学习策略以实现高效的抓举模式。大量实验表明,该设计结合所学技能,对未见物体具有更强泛化能力,显著优于现有方案。此外,物理样机在真实场景中实现了良好的仿真到现实迁移效果,验证了其实际应用价值。演示视频详见:https://github.com/siggraph-pin-pression-gripper/pin-pression-gripper-video。

原文摘要 · Abstract (English)

We introduce a novel design of parallel-jaw grippers drawing inspiration from pin-pression toys. The proposed pin-pression gripper features a distinctive mechanism in which each finger integrates a 2D array of pins capable of independent extension and retraction. This unique design allows the gripper to instantaneously customize its finger's shape to conform to the object being grasped by dynamically adjusting the extension/retraction of the pins. In addition, the gripper excels in in-hand re-orientation of objects for enhanced grasping stability again via dynamically adjusting the pins. To learn the dynamic grasping skills of pin-pression grippers, we devise a dedicated reinforcement learning algorithm with careful designs of state representation and reward shaping. To achieve a more efficient grasp-while-lift grasping mode, we propose a curriculum learning scheme. Extensive evaluations demonstrate that our design, together with the learned skills, leads to highly flexible and robust grasping with much stronger generality to unseen objects than alternatives. We also highlight encouraging physical results of sim-to-real transfer on a physically manufactured pin-pression gripper, demonstrating the practical significance of our novel gripper design and grasping skill. Demonstration videos for this paper are available at https://github.com/siggraph-pin-pression-gripper/pin-pression-gripper-video.

机器人抓取自适应夹持强化学习

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