Soft Regrasping Tool Inspired by Jamming Gripper

2025-09-17

AI Summary

Traditional rigid grippers suffer from poor adaptability and require part-specific customization, failing to mitigate pose uncertainty in robotic assembly. To address this, we propose a vacuum-driven soft gripper inspired by jamming-phase transitions. Its core innovation lies in dynamically forming adaptive cavities via the jamming effect of soft membranes, coupled with a triangular-pyramid indentation-depth optimization strategy for geometry-guided cavity shaping. This approach simultaneously achieves high positioning accuracy (improved repeatability) and broad grasp accessibility, overcoming the universality limitation of rigid grippers. Experiments on ten mechanical parts demonstrate an overall regrasping-and-placement success rate exceeding 80%, reaching over 90% for cylindrical parts—significantly enhancing assembly robustness and generalization capability.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Regrasping on fixtures is a promising approach to reduce pose uncertainty in robotic assembly, but conventional rigid fixtures lack adaptability and require dedicated designs for each part. To overcome this limitation, we propose a soft jig inspired by the jamming transition phenomenon, which can be continuously deformed to accommodate diverse object geometries. By pressing a triangular-pyramid-shaped tool into the membrane and evacuating the enclosed air, a stable cavity is formed as a placement space. We further optimize the stamping depth to balance placement stability and gripper accessibility. In soft-jig-based regrasping, the key challenge lies in optimizing the cavity size to achieve precise dropping; once the part is reliably placed, subsequent grasping can be performed with reduced uncertainty. Accordingly, we conducted drop experiments on ten mechanical parts of varying shapes, which achieved placement success rates exceeding 80% for most objects and above 90% for cylindrical ones, while failures were mainly caused by geometric constraints and membrane properties. These results demonstrate that the proposed jig enables general-purpose, accurate, and repeatable regrasping, while also clarifying its current limitations and future potential as a practical alternative to rigid fixtures in assembly automation.

Problem

• Developing adaptable soft jig for robotic regrasping
• Optimizing cavity size to ensure precise part placement
• Reducing pose uncertainty in robotic assembly tasks

Innovation

• Soft jig using jamming transition for adaptability
• Triangular-pyramid tool forms stable cavity via evacuation
• Optimized stamping depth balances stability and accessibility