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

TL;DR

This paper introduces a modular soft gripper with whole-body proprioceptive morphing, enabling adaptive reconfiguration for diverse and cross-scale grasping tasks, inspired by biological systems like octopuses.

Contribution

It presents a novel distributed network of self-sensing pneumatic actuators that can reconfigure the entire gripper topology for versatile manipulation.

Findings

Expands grasping envelope and object diversity handling.

Enables manipulation across scales up to 10×.

Supports multi-object and internal hook grasping modes.

Abstract

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.