A Support-Enhanced Granular-Jamming Gripper for RL-based Grasping with Continuum Manipulators

2026-09-24

AI Summary

This study addresses the challenge of achieving reliable rigid grasping with continuum robots, which is hindered by structural compliance and modeling inaccuracies. To overcome these limitations, this work proposes a paradigm that integrates physical principles with embodied intelligence. Specifically, a support-enhanced granular jamming gripper is developed, synergizing visual feedback, pneumatic control, and reinforcement learning strategies to substantially reduce reliance on high-precision modeling. By training the policy in randomized simulations and successfully deploying it onto physical systems, the effectiveness of this modular grasping framework is validated. The proposed approach enables stable and robust grasping for continuum robots, demonstrating strong potential for practical applications where accurate analytical models are difficult to obtain.

Abstract

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.

Problem

• Continuum manipulators
• Granular-jamming gripper
• Reinforcement learning
• Grasping
• Structural compliance

Innovation

• Granular-jamming gripper
• Continuum manipulator
• Reinforcement learning
• Sim-to-Real
• Embodied intelligence