Extending Planar Orientation Control Range of Grasped Objects Using Selective Actuation in a Redundant Parallel-Driven Soft Gripper

Design of a soft gripper with a changeable adapter for enhanced grasping and manipulation

: Soft grippers utilize compliant materials to achieve adaptable grasping, yet they often face challenges in accommodating objects with widely varying dimensions due to their fixed kinematic structures. This paper presents the design, fabrication, modeling, and control of a novel soft gripper featuring a rigid-flexible coupled variable range adapter. By integrating a motorized crank-slider mechanism with soft pneumatic fingers, the gripper achieves a dynamic volumetric workspace capable of manipulating objects ranging from compact to large geometries. Theoretical modeling and experimental characterization reveal that the adapter serves a dual purpose: it not only expands the effective workspace but also functions as a mechanical force amplifier, capable of exponentially boosting the contact force through kinematic reconfiguration. Furthermore, an intuitive Human-in-the-Loop (HITL) teleoperation strategy is established using wearable flex sensors. This control framework maps human gestures to robotic actuation, leveraging human visual feedback as a high-level perception loop to validate the open-loop response of the soft actuators. Experimental results demonstrate that this integrated system significantly improves adaptability and payload stability for diverse object geometries compared to fixed-base counterparts.

A modular soft gripper with enhanced in-hand manipulation capabilities

Soft robotic grippers offer exceptional adaptability and safety for grasping diverse objects. However, achieving dexterous in-hand manipulation remains a significant challenge. Traditional tendon-driven designs are optimized for stable holding but lack the intrinsic degrees of freedom (DoF) required for dexterous object reorientation, often relying instead on complex hybrid mechanisms or repeated regrasping. This study presents a novel modular soft robotic gripper specifically designed to bridge the gap between stable grasping and active, continuous in-hand manipulation. The system features three independently actuated soft fingers, each providing three DoF through a combination of asymmetric tendon-driven bending and active base rotation. A core principle of this design is an asymmetric soft joint that exploits passive geometric blocking to provide direction-dependent compliance and mechanical protection against excessive deformation. By decoupling the bending and spatial reorientation motions, the gripper generates coordinated kinematic patterns across the fingers. Experimental validation demonstrates the system’s ability to robustly grasp objects spanning a wide spectrum of sizes and stiffnesses using a unified actuation strategy. Furthermore, the gripper successfully performs complex in-hand manipulation tasks, specifically, continuous object rotation along its axis without releasing the initial grasp. These results indicate that the proposed modular architecture significantly extends the dexterity of purely tendon-driven soft grippers while preserving their inherent compliance, scalability, and mechanical simplicity.

Control Architecture for Safe Grasping of Fragile Objects Using a Coarse Position-Controlled Gripper

Robots are increasingly used in unstructured environments. The need for them to safely grasp unknown objects without damaging them becomes crucial. Humans achieve this by sensing and quickly responding by adjusting their grasping force. Similarly, effective grasp acquisition in robots requires compliant interaction strategies that can adapt to uncertain object properties and adjust to any instabilities during manipulation. We present a geometry-aware force/torque-based contact estimation method for a coarse position-controlled gripper, combined with an adaptive admittance controller for safe grasp acquisition. The desired contact forces are estimated online to keep stable contact with objects of unknown properties. This enables compliant and stable grasps while avoiding excessive forces. Experiments with objects of different sizes, shapes, stiffnesses, and weights show that the proposed algorithm not only prevents slippage but also applies minimal force to safely grasp an object without causing excessive deformation.

Active Surface-Driven Reconfigurable Gripper: Robust Grasping and Sequential Manipulation of Thin Objects

Robotic grippers face substantial challenges in grasping and manipulating thin objects. Most existing grippers rely on highly precise approach and grasp motions, which limits robustness and reduces applicability. This paper explores thin-object grasping using books as a representative example. Here, we propose a novel solution that integrates an active surface with underactuated compliance to achieve stable grasping of thin objects without complex control. First, an underactuated gripper with an active surface is designed. The active-surface thumb performs in-hand repositioning of the target book without requiring adjustments of the robot arm or the other fingers, while the underactuated fingers establish compliant contact conditions with the environment, and the reconfigurable structure enables reliable grasping of books under different configurations. Second, we establish a kinematic model of the gripper, and determine the initial grasp postures for two representative scenarios (books lying flat on a desktop and books vertically packed in a shelf). Third, by analyzing the physical model of a book lying on a table and its interaction with the gripper and the environment, we systematically optimize the structural parameters and grasping strategy. Finally, extensive experiments validate the effectiveness of the proposed gripper and strategy. The results demonstrate strong robustness and adaptability when grasping thin objects placed flat (including books, paper, fabric, plastic film, and mouse pad), as well as a high success rate when grasping vertically packed books. Moreover, the proposed gripper can reliably complete long sequential"grasp-place"tasks.

Active Surface-Driven

Design of an Extensible Central Backbone for Stiffness-Shifting in Soft Robotic Arms

Soft robotic arms, which are most commonly constructed from fluid-driven actuators, have demonstrated a high degree of compliance and the ability to move in simple and complex ways, from single bends to whole-arm gripping to drawer opening. However, their practical applications have been constrained by limited load-bearing capacities: soft arms rarely lift more than 3 N in cross workspace movements. Designing soft robotic arms to have a high load-bearing capacity is an unsolved challenge, as simply increasing actuation pressure dramatically reduces compliance at the arm’s surface. Antagonistic actuation (without a backbone) has shown promise, but is inherently limited to the available actuation pressures, while actuation against an inextensible backbone - similar to hyper-redundant manipulators - locks out axial deformation. In this article, we propose a third option: a passive, extensible backbone constructed from a tailorable architected geometry that adjusts stiffness, retains the ability to change length, and is compatible with unidirectional or antagonistic actuation. Our design consists of a multilayer stack of sinusoidal wave-shapes, inspired by recently developed trimmed helicoids, which shifts the center point of any soft arm’s stiffness range while otherwise preserving actuatable degrees of freedom. We characterize bending, axial, and shear stiffness through finite element analysis validated with prototype tests, and further experimentally show how these backbones can be used to improve load bearing capacity. Our results provide a general structural reinforcement strategy for soft robotic arms and demonstrate the potential for using our structure or similar structures to augment existing arm designs.