In the horticultural world, some vines are especially grabby. As they grow, the woody tendrils can wrap around obstacles with enough force to pull down entire fences and trees. Inspired by vines' twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed. The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out.

Soft robots are powerful tools for manipulating delicate objects, yet their adoption is hindered by two gaps: the lack of integrated tactile sensing and sensor signal distortion caused by actuator deformations. This paper addresses these challenges by introducing the SoftMag actuator: a magnetic tactile-sensorized soft actuator. Unlike systems relying on attached sensors or treating sensing and actuation separately, SoftMag unifies them through a shared architecture while confronting the mechanical parasitic effect, where deformations corrupt tactile signals. A multiphysics simulation framework models this coupling, and a neural-network-based decoupling strategy removes the parasitic component, restoring sensing fidelity. Experiments including indentation, quasi-static and step actuation, and fatigue tests validate the actuator's performance and decoupling effectiveness. Building upon this foundation, the system is extended into a two-finger SoftMag gripper, where a multi-task neural network enables real-time prediction of tri-axial contact forces and position. Furthermore, a probing-based strategy estimates object firmness during grasping. Validation on apricots shows a strong correlation (Pearson r over 0.8) between gripper-estimated firmness and reference measurements, confirming the system's capability for non-destructive quality assessment. Results demonstrate that combining integrated magnetic sensing, learning-based correction, and real-time inference enables a soft robotic platform that adapts its grasp and quantifies material properties. The framework offers an approach for advancing sensorized soft actuators toward intelligent, material-aware robotics.

Abstract-- Soft robotics gripper have shown great promise in handling fragile and geometrically complex objects. However, most existing solutions rely on bulky actuators, complex control strategies, or advanced tactile sensing to achieve stable and reliable grasping performance. In this work, we present a multi-finger hybrid gripper featuring passively deformable origami modules that generate constant force and torque output. Each finger composed of parallel origami modules is driven by a 1-DoF actuator mechanism, enabling passive shape adaptability and stable grasping force without active sensing or feedback control. More importantly, we demonstrate an interesting capability in simultaneous multi-object grasping, which allows stacked objects of varied shape and size to be picked, transported and placed independently at different states, significantly improving manipulation efficiency compared to single-object grasping. As robotics continues to expand beyond industrial automation into unstructured environments and daily tasks, there is a growing demand for efficient grippers that can handle objects with varying geometries and stiffness, and even multi-object grasping [1].

Abstract-- Fruit harvesting remains predominantly a labor-intensive process, motivating the development of research for robotic grippers. Conventional rigid or vacuum-driven grippers require complex mechanical design or high energy consumption. Current enveloping-based fruit harvesting grippers lack adaptability to fruits of different sizes. This paper introduces a drawstring-inspired, cable-driven soft gripper for versatile and gentle fruit harvesting. The design employs 3D-printed Thermoplastic Polyurethane (TPU) pockets with integrated steel wires that constrict around the fruit when actuated, distributing pressure uniformly to minimize bruising and allow versatility to fruits of varying sizes. The lightweight structure, which requires few components, reduces mechanical complexity and cost compared to other grippers. Actuation is achieved through servo-driven cable control, while motor feedback provides autonomous grip adjustment with tunable grip strength. Experimental validation shows that, for tomatoes within the gripper's effective size range, harvesting was achieved with a 0% immediate damage rate and a bruising rate of less than 9% after five days, reinforcing the gripper's suitability for fruit harvesting. While there is ongoing research and development towards fruit harvesting solutions [1] [2], hand-picking remains the dominant method due to its delicacy for soft fruits [3].

Abstract: Properly handling del i cate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing . Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product . In this work, a flexible pressure sensor that is both low cost and easy to fabricate is integrated with robotic grippers for work ing with produce of varying shape s, sizes, and stiffness es . The sensor is successfully integrated with both a rigid robotic gripper, as well as a pneumatically actuated soft finger. Furthermore, an algorithm is proposed for acce lerated estimation of the steady - state value of the sensor output based on the transient response data, to enable real - time applications. The sensor is shown to be effective in incorporating feedback to correctly grasp objects of unknown sizes and stiffnesses . At the same time, the sensor provid es estimates for these values which can be utilized for identification of qualities such as ripeness levels and bruising . It is also shown to be able to provide force feedback for objects of variable stiffness es . Th is enables future use not only for produce identification, but also for tasks such as quality control and selective distribution based on ripeness levels . Keywords: Robotics, sensing, p roduce handling, grasping Highlights: Low - cost and easy - to - fabricate sensor for easy implementation with a variety of robotic grippers Fast estimation of settled resistance using exponential decay curve fit Measurements of grasping force and stiffness of a held object V arious produce handling features such as ripeness monitoring, bruising detection, and size estimation 1. Introduction: The use of robotic end - effectors for securely grasping objects is a pivotal component in manipulation tasks .

We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers. Second, we devise theoretical models of kinematics and power transmission based on the proposed gripper, accurately obtaining fingertip positions and contact forces. Through coupling and decoupling of five-linkage mechanisms, the proposed gripper offers the expected capabilities of grasping payload/force/stability and objects with large dimension ranges. Third, to realize the force control, an LSTM model is proposed to determine the grasping mode for synthesizing force-feedback control policies that exploit contact sensing after outlining the uncertainty of currents using a statistical method. Finally, a series of experiments are implemented to measure quantitative indicators, such as the payload, grasping force, force sensing, grasping stability and the dimension ranges of objects to be grasped. Additionally, the grasping performance of the proposed gripper is verified experimentally to guarantee the high versatility and robustness of the proposed gripper.

Robotic grasping and manipulation in underwater environments present unique challenges for robotic hands traditionally used on land. These challenges stem from dynamic water conditions, a wide range of object properties from soft to stiff, irregular object shapes, and varying surface frictions. One common approach involves developing finger-based hands with embedded compliance using underactuation and soft actuators. This study introduces an effective alternative solution that does not rely on finger-based hand designs. We present a fish mouth inspired origami gripper that utilizes a single degree of freedom to perform a variety of robust grasping tasks underwater. The innovative structure transforms a simple uniaxial pulling motion into a grasping action based on the Yoshimura crease pattern folding. The origami gripper offers distinct advantages, including scalable and optimizable design, grasping compliance, and robustness, with four grasping types: pinch, power grasp, simultaneous grasping of multiple objects, and scooping from the seabed. In this work, we detail the design, modeling, fabrication, and validation of a specialized underwater gripper capable of handling various marine creatures, including jellyfish, crabs, and abalone. By leveraging an origami and bio-inspired approach, the presented gripper demonstrates promising potential for robotic grasping and manipulation in underwater environments.

We propose a novel system for robot-to-human object handover that emulates human coworker interactions. Unlike most existing studies that focus primarily on grasping strategies and motion planning, our system focus on 1. inferring human handover intents, 2. imagining spatial handover configuration. The first one integrates multimodal perception-combining visual and verbal cues-to infer human intent. The second one using a diffusion-based model to generate the handover configuration, involving the spacial relationship among robot's gripper, the object, and the human hand, thereby mimicking the cognitive process of motor imagery. Experimental results demonstrate that our approach effectively interprets human cues and achieves fluent, human-like handovers, offering a promising solution for collaborative robotics. Code, videos, and data are available at: https://i3handover.github.io.

-- Soft robotic grippers gently and safely manipulate delicate objects due to their inherent adaptability and softness. Limited by insufficient stiffness and imprecise force control, conventional soft grippers are not suitable for applications that require stable grasping force. In this work, we propose a soft gripper that utilizes an origami-inspired structure to achieve tunable constant force output over a wide strain range. The geometry of each taper panel is established to provide necessary parameters such as protrusion distance, taper angle, and crease thickness required for 3D modeling and FEA analysis. Simulations and experiments show that by optimizing these parameters, our design can achieve a tunable constant force output. Moreover, the origami-inspired soft gripper dynamically adapts to different shapes while preventing excessive forces, with potential applications in logistics, manufacturing, and other industrial settings that require stable and adaptive operations.

This study presents an innovative offset-trimmed helicoids (OTH) structure, featuring a tunable deformation center that emulates the flexibility of human fingers. This design significantly reduces the actuation force needed for larger elastic deformations, particularly when dealing with harder materials like thermoplastic polyurethane (TPU). The incorporation of two helically routed tendons within the finger enables both in-plane bending and lateral out-of-plane transitions, effectively expanding its workspace and allowing for variable curvature along its length. Compliance analysis indicates that the compliance at the fingertip can be fine-tuned by adjusting the mounting placement of the fingers. This customization enhances the gripper's adaptability to a diverse range of objects. By leveraging TPU's substantial elastic energy storage capacity, the gripper is capable of dynamically rotating objects at high speeds, achieving approximately 60 in just 15 milliseconds. The three-finger gripper, with its high dexterity across six degrees of freedom, has demonstrated the capability to successfully perform intricate tasks. One such example is the adept spinning of a rod within the gripper's grasp.