--Incipient slip detection provides critical feedback for robotic grasping and manipulation tasks. However, maintaining its adaptability under diverse object properties and complex working conditions remains challenging. This article highlights the importance of completely representing spatiotemporal features of slip, and proposes a novel approach for incipient slip modeling and detection. Based on the analysis of localized displacement phenomenon, we establish the relationship between the characteristic strain rate extreme events and the local slip state. This approach enables the detection of both the spatial distribution and temporal dynamics of stick -slip regions. Also, the proposed method can be applied to strain distribution sensing devices, such as vis ion-based tactile sensors. Simulations and prototype experiments validated the effectiveness of this approach under varying contact conditions, including different contact geometries, friction coefficients, and combined loads. Experiments demonstrated that this method not only accurately and reliably delineates incipient slip, but also facilitates friction parameter estimation and adaptive grasping control. INTRODUCTION ACTILE perception plays a crucial role in stable grasping and dexterous manipulation in humans [1]. Neuroscientific studies show that humans can identify the frictional parameters of objects they touch with over 90% accuracy [2], and quickly adjust the grasp force within about 200 milliseconds to prevent slipping [3]. This ability enables humans to adapt to changes in friction levels based on tactile feedback and apply proper force to ensure s tability while maintaining gentle grasping [4]. The perception of incipient slip is an effective means for friction parameter recognition and grasp force control [5],[6]. Incipient slip is an intermediate state between complete sticking and full slipping of the contact surface, as shown in Figure 1. When a tangential load is applied to the contact surface, slip first occurs at the contact edge. It gradually spreads inward, eventually covering the entire stick region [7]. This work was supported by the National Natural Science Foundation of China under Grant 52375017. We refer to these two characteristics of incipient slip as spatial and temporal characteristics: spatial characteristics refer to the distribution of the stick -slip reg ion at a given moment, while temporal characteristics describe the time evolution of local slip. These characteristics are widely present in human tactile perception. According to existing research, Human sensory information is encoded by neural populations to capture spatial distribution, rather than being transmitted by individual neurons. Besides, skin deformation can be influenced by the loading history [9].

Accurate grasp force control is one of the key skills for ensuring successful and damage-free robotic grasping of objects. Although existing methods have conducted in-depth research on slip detection and grasping force planning, they often overlook the issue of adaptive tracking of the actual force to the target force when handling objects with different material properties. The optimal parameters of a force tracking controller are significantly influenced by the object's stiffness, and many adaptive force tracking algorithms rely on stiffness estimation. However, real-world objects often exhibit viscous, plastic, or other more complex nonlinear time-varying behaviors, and existing studies provide insufficient support for these materials in terms of stiffness definition and estimation. To address this, this paper introduces the concept of generalized stiffness, extending the definition of stiffness to nonlinear time-varying grasp system models, and proposes an online generalized stiffness estimator based on Long Short-Term Memory (LSTM) networks. Based on generalized stiffness, this paper proposes an adaptive parameter adjustment strategy using a PI controller as an example, enabling dynamic force tracking for objects with varying characteristics. Experimental results demonstrate that the proposed method achieves high precision and short probing time, while showing better adaptability to non-ideal objects compared to existing methods. The method effectively solves the problem of grasp force tracking in unknown, nonlinear, and time-varying grasp systems, enhancing the robotic grasping ability in unstructured environments.

Humans have the remarkable ability to pick up unfamiliar objects and quickly understand their surface properties, such as friction, and dynamics. This knowledge enables us not only to reorient objects using our arms but also to manipulate them within our hands, extending our capabilities beyond what is typically seen in traditional robotics. In this paper, we introduce a custom parallel gripper equipped with commercial 6-degree-of-freedom (DoF) force-torque (F/T) sensors and custom relative velocity sensors (see Figure 1), for in-hand slip-aware control that relies solely on in-hand sensing. The ability to independently measure force and planar velocity introduces new opportunities for intricate robotic manipulation. This hardware combination enables rapid estimation of friction and contact surface properties without the need for external sensors, thus facilitating for precise in-hand manipulation of objects in both rotational and translational movements. Slip-aware control significantly enhances the functionality of robotic manipulators by enabling the object-end-effector relative pose to adapt during grasping, thereby extending the operational workspace. This adaptability is particularly valuable in constrained environments, where the manipulator's movement is limited, or for intelligent human-robot interaction, enabling for instance more intuitive and safe handovers. Furthermore, in-hand slippage control opens up new opportunities for multi-arm manipulation of single objects, allowing for the repositioning of grasps without releasing the object, thereby enabling more efficient and flexible handling of larger items. Our system has been rigorously tested across a wide range of experiments, demonstrating its effectiveness and versatility.

Humans can steadily and gently grasp unfamiliar objects based on tactile perception. Robots still face challenges in achieving similar performance due to the difficulty of learning accurate grasp-force predictions and force control strategies that can be generalized from limited data. In this article, we propose an approach for learning grasping from ideal force control demonstrations, to achieve similar performance of human hands with limited data size. Our approach utilizes objects with known contact characteristics to automatically generate reference force curves without human demonstrations. In addition, we design the dual convolutional neural networks (Dual-CNN) architecture which incorporating a physics-based mechanics module for learning target grasping force predictions from demonstrations. The described method can be effectively applied in vision-based tactile sensors and enables gentle and stable grasping of objects from the ground. The described prediction model and grasping strategy were validated in offline evaluations and online experiments, and the accuracy and generalizability were demonstrated.

Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for non-gaited legged locomotion that utilizes internal force control, similar to a multi-fingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We use a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. Additionally, we use a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.

Data gloves play a crucial role in study of human grasping, and could provide insights into grasp synergies. Grasp synergies lead to identification of underlying patterns to develop control strategies for hand exoskeletons. This paper presents the design and implementation of a data glove that has been enhanced with instrumentation and fabricated using 3D printing technology. The glove utilizes flexible sensors for the fingers and force sensors integrated into the glove at the fingertips to accurately capture grasp postures and forces. Understanding the kinematics and dynamics of human grasp including reach-to-grasp is undertaken. A comprehensive study involving 10 healthy subjects was conducted. Grasp synergy analysis is carried out to identify underlying patterns for robotic grasping. The t-SNE visualization showcased clusters of grasp postures and forces, unveiling similarities and patterns among different GTs. These findings could serve as a comprehensive guide in design and control of tendon-driven soft hand exoskeletons for rehabilitation applications, enabling the replication of natural hand movements and grasp forces.

Individuals with hand paralysis resulting from C6-C7 spinal cord injuries frequently rely on tenodesis for grasping. However, tenodesis generates limited grasping force and demands constant exertion to maintain a grasp, leading to fatigue and sometimes pain. We introduce the MyHand-SCI, a wearable robot that provides grasping assistance through motorized exotendons. Our user-driven device enables independent, ipsilateral operation via a novel Throttle-based Wrist Angle control method, which allows users to maintain grasps without continued wrist extension. A pilot case study with a person with C6 spinal cord injury shows an improvement in functional grasping and grasping force, as well as a preserved ability to modulate grasping force while using our device, thus improving their ability to manipulate everyday objects. This research is a step towards developing effective and intuitive wearable assistive devices for individuals with spinal cord injury.

Abstract-- This paper proposes a novel method to refine the 6D pose estimation inferred by an instance-level deep neural network which processes a single RGB image and that has been trained on synthetic images only. The proposed optimization algorithm usefully exploits the depth measurement of a standard RGB-D camera to estimate the dimensions of the considered object, even though the network is trained on a single CAD model of the same object with given dimensions. The improved accuracy in the pose estimation allows a robot to grasp apples of various types and significantly different dimensions successfully; this was not possible using the standard pose estimation algorithm, except for the fruits with dimensions very close to those of the CAD drawing used in the training process. Grasping fresh fruits without damaging each item also demands a suitable grasp force control. A parallel gripper equipped with special force/tactile sensors is thus adopted to achieve safe grasps with the minimum force necessary to lift the fruits without any slippage and any deformation at the same time, with no knowledge of their weight. I. INTRODUCTION Having the ability to estimate the position and orientation of an object in space is a critical aspect for the autonomous This problem becomes very challenging if the objects of interest are natural objects, such as fruits or vegetables, due to the high variability of their shapes and dimensions.