Grasping Tasks Research Articles

A Lightweight Detection Model Without Convolutions for Complex Stacked Grasping Tasks

In complex environments with multi-object stacking, the spatial relationships between objects necessitate a sequential grasping strategy to ensure both the safety of the target objects and the efficiency of robotic arm operations. To address this challenge, this study introduces a Visual Manipulation Relationship Network (VMRN) to determine the optimal grasping sequence. Traditional VMRN frameworks typically rely on convolutional neural networks (CNNs) for feature extraction, which often struggle with high-frequency feature extraction, long-tail data distributions, and real-time computational demands in multi-object stacking scenarios. To overcome these limitations, we propose a lightweight, convolution-free Transformer-based feature extraction network integrated into the visual detection model. This model is specifically designed for visual reasoning, with a focus on lightweight optimization to enhance the extraction of features for stacked objects. The proposed network incorporates local window attention, global information aggregation and broadcasting, and a dual-dimensional attention-based feedforward network to improve feature representation. Additionally, a novel loss function is designed to address the performance degradation in detecting long-tail categories, effectively mitigating the over-suppression of rare objects in imbalanced datasets. Experimental results demonstrate that the proposed model significantly improves both detection accuracy and computational efficiency, making it particularly suitable for real-time robotic grasping tasks in complex environments due to its lightweight design.

Planning and Motion Control for Underwater Bimanual Soft Manipulator in Underwater Grasping Task

Planning and Motion Control for Underwater Bimanual Soft Manipulator in Underwater Grasping Task

Design and Control of a Tendon-Driven Robotic Finger Based on Grasping Task Analysis.

To analyze the structural characteristics of a human hand, data collection gloves were worn for typical grasping tasks. The hand manipulation characteristics, finger end pressure, and finger joint bending angle were obtained via an experiment based on the Feix grasping spectrum. Twelve types of tendon rope transmission paths were designed under the N + 1 type tendon drive mode, and the motion performance of these 12 types of paths applied to tendon-driven fingers was evaluated based on the evaluation metric. The experiment shows that the designed tendon path (d) has a good control effect on the fluctuations of tendon tension (within 0.25 N), the tendon path (e) has the best control effect on the joint angle of the tendon-driven finger, and the tendon path (l) has the best effect on reducing the friction between the tendon and the pulley. The obtained tendon-driven finger motion performance model based on 12 types of tendon paths is a good reference value for subsequent tendon-driven finger structure design and control strategies.

Age Differences in a Combined Walking and Grasping Task

Previous studies have identified patterns of coordinated control when adults combine gait and grasping. What remains unclear is whether the coordination of these two tasks differs between adolescent and adult groups. Groups of adults and adolescents were asked to walk across an instrumented gait mat in three conditions: walk forward, walk and grasp a small target, and walk and grasp a large target. Spatiotemporal measures of gait/gait variability and grasp/grasp variability were quantified. Both adolescents and young adults exhibited decreased velocity, decreased step-extremity ratio, and increased percent of gait cycle spent in double support when grasping compared to walking alone. The major influence of grasping was seen during the final step before grasp in both groups. Change scores between walk forward and walk and grasp conditions were larger for adolescents. Furthermore, spatiotemporal measures of gait and grasping were more variable in adolescents. These results suggest that superimposing grasp onto gait is more challenging for adolescents than young adults. The challenges associated with combining these two tasks is particularly evident in the last step prior to object contact and suggests that the increased planning and execution demands required to perform these coordinated skills affects adolescents to a greater extent than young adults.

Biomimetic Structure and Surface for Grasping Tasks.

Under water, on land, or in the air, creatures use a variety of grasping methods to hunt, avoid predators, or carry food. Numerous studies have been conducted to construct a bionic surface for grasping tasks. This paper reviews the typical biomimetic structures and surfaces (wedge-shaped surface, suction cup surface and thorn claw surface) for grasping scenarios. Initially, progress in gecko-inspired wedge-shaped adhesive surfaces is reviewed, encompassing the underlying mechanisms that involve tuning the contact area and peeling behavior. The applications of grippers utilizing this adhesive technology are also discussed. Subsequently, the suction force mechanisms and applications of surfaces inspired by octopus and remora suction cups are outlined. Moreover, this paper introduces the applications of robots incorporating the principles of beetle-inspired and bird-inspired thorn claw structures. Lastly, inspired by remoras' adhesive discs, a composite biomimetic adhesive surface is proposed. It integrates features from wedge-shaped, suction cup, and claw thorn surfaces, potentially surpassing the adaptability of basic bioinspired surfaces. This surface construction method offers a potential avenue to enhance adhesion capabilities with superior adaptability to surface roughness and curvature.

Decoding Electroencephalography Underlying Natural Grasp Tasks across Multiple Dimensions

Individuals suffering from motor dysfunction due to various diseases often face challenges in performing essential activities such as grasping objects using their upper limbs, eating, writing, and more. This limitation significantly impacts their ability to live independently. Brain–computer interfaces offer a promising solution, enabling them to interact with the external environment in a meaningful way. This exploration focused on decoding the electroencephalography of natural grasp tasks across three dimensions: movement-related cortical potentials, event-related desynchronization/synchronization, and brain functional connectivity, aiming to provide assistance for the development of intelligent assistive devices controlled by electroencephalography signals generated during natural movements. Furthermore, electrode selection was conducted using global coupling strength, and a random forest classification model was employed to decode three types of natural grasp tasks (palmar grasp, lateral grasp, and rest state). The results indicated that a noteworthy lateralization phenomenon in brain activity emerged, which is closely associated with the right or left of the executive hand. The reorganization of the frontal region is closely associated with external visual stimuli and the central and parietal regions play a crucial role in the process of motor execution. An overall average classification accuracy of 80.3% was achieved in a natural grasp task involving eight subjects.

BCI Control of a Robotic arm based on SSVEP with Moving Stimuli for Reach and grasp Tasks.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

Measuring Arm and Hand Joint Kinematics to Estimate Impairment During a Functional Reach and Grasp Task after Stroke

Background Current approaches to characterizing deficits in upper limb movements after stroke typically focus either on changes in a functional measure, for example, how well a patient can complete a task, or changes in impairment, for example, isolated measurements of joint range of motion. However, there can be notable dissociations between static measures of impairment versus those of function. Objective We develop a method to measure upper limb joint angles during performance of a functional task and use measurements to characterize joint impairment in the context of a functional task. Methods We developed a sensorized glove that can precisely measure select finger, hand, and arm joints while participants complete a functional reach-to-grasp task involving manipulation of a sensorized object. Results We first characterized the accuracy and precision of the glove’s joint angle measurements. We then measured joint angles in neurologically intact participants (n = 4 participants, 8 limbs) to define the expected distribution of joint angle variation during task execution. These distributions were used to normalize finger, hand, and arm joint angles in stroke participants (n = 6) as they performed the task. We present a participant-specific visualization of functional joint angle variance which illustrated that stroke participants with nearly identical clinical scores exhibited unique patterns of joint angle variation. Conclusions Overall, measuring individual joint angles in the context of a functional task may inform whether changes in functional scores over recovery or rehabilitation are driven by changes in impairment or the development of compensatory strategies, and provide a quantified path toward personalized rehabilitative therapy.

Measures of Maximal Tactile Pressures during a Sustained Grasp Task Using a TactArray Device Have Satisfactory Reliability and Concurrent Validity in People with Stroke.

Sensor-based devices can record pressure or force over time during grasping and therefore offer a more comprehensive approach to quantifying grip strength during sustained contractions. The objectives of this study were to investigate the reliability and concurrent validity of measures of maximal tactile pressures and forces during a sustained grasp task using a TactArray device in people with stroke. Participants with stroke (n = 11) performed three trials of sustained maximal grasp over 8 s. Both hands were tested in within- and between-day sessions, with and without vision. Measures of maximal tactile pressures and forces were measured for the complete (8 s) grasp duration and plateau phase (5 s). Tactile measures are reported using the highest value among three trials, the mean of two trials, and the mean of three trials. Reliability was determined using changes in mean, coefficients of variation, and intraclass correlation coefficients (ICCs). Pearson correlation coefficients were used to evaluate concurrent validity. This study found that measures of reliability assessed by changes in means were good, coefficients of variation were good to acceptable, and ICCs were very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s in the affected hand with and without vision for within-day sessions and without vision for between-day sessions. In the less affected hand, changes in mean were very good, coefficients of variations were acceptable, and ICCs were good to very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s and 5 s, respectively, in between-day sessions with and without vision. Maximal tactile pressures had moderate correlations with grip strength. The TactArray device demonstrates satisfactory reliability and concurrent validity for measures of maximal tactile pressures in people with stroke.

Decoding of Brain Functional Connections Underlying Natural Grasp Task Using Time-Frequency Cross Mutual Information

Decoding of Brain Functional Connections Underlying Natural Grasp Task Using Time-Frequency Cross Mutual Information

Effects of Feedback of Fingertip Force Information with Temporal Coded Vibration Stimulation on Precision Grasping Tasks

Tactile information plays an important role in human manipulation of objects; however, prosthetic limb placement or teleoperation requires the manipulation of alternative bodies in the absence of tactile sensations. To provide an alternative to tactile sensation, this study proposes and assesses a continuous feedback scheme with temporally coded vibration. This scheme was designed to provide discrete intended tactile information in response to changing object-controlled situations by repetitively presenting time-coded vibration patterns. The effects of the proposed scheme on an object with acatch-and-hold task in virtual reality were confirmed. Compared to the control feedback scheme that provides vibration only when the balance of the virtual grip force and object position is changed, the proposed feedback scheme has a better effect in terms of the success rate of holding on to the object with an appropriate holding force during the task. The effect is larger, especially in the invisible task condition, suggesting that the increased amount of information with coded vibration patterns can be used without any special training, especially without visual information. Considering the existing studies that show the effect of a feedback scheme in response to motion events, the continuous feedback scheme proposed in this study may be more suitable for movements that require sequential coordination and passive responses than stimulation methods based on motion events. This feedback scheme has potential applications not only in tele-technology but also in healthcare, such as rehabilitation.

Control Strategy of an Underactuated Underwater Drone-Shape Robot for Grasping Tasks.

In underwater environments, ensuring people's safety is complicated, with potentially life-threatening outcomes, especially when divers have to work in deeper conditions. To improve the available solutions for working with robots in this kind of environment, we propose the validation of a control strategy for robots when taking objects from the seabed. The control strategy proposed is based on acceleration feedback in the model of the system. Using this model, the reference values for position, velocity and acceleration are estimated, and then the position error signal can be computed. When the desired position is obtained, it is possible to then obtain the position error. The validation was carried out using three different objects: a ball, a bottle, and a plant. The experiment consisted of using this control strategy to take those objects, which the robot carried for a moment to validate the stabilisation control and reference following the control in terms of angle and depth. The robot was operated by a pilot from outside of the pool and was guided using a camera and sonar in a teleoperated way. As an advantage of this control strategy, the model upon which the robot is based is decoupled, allowing control of the robot for each uncoupled plane, this being the main finding of these tests. This demonstrates that the robot can be controlled by a control strategy based on a decoupled model, taking into account the hydrodynamic parameters of the robot.

Resonant Pneumatic Tactile Sensing for Soft Grippers

Soft robots capable of dexterous manipulation can enable the exploration of extreme environments. Equipping these robots with tactile sensing is a challenge, as sensors must be flexible, stretchable, and robust to environmental conditions. We present a tactile sensor design with a pneumatically driven acoustic resonator, without electronics near from the end-effector. For applications to soft grippers, we measure the resonant frequency of a soft tube undergoing stretching and bending. A small hole along the resonant tube enables contact sensing and pretouch up to 2 mm away. We also measure resonant frequency for a rigid uni-axial force sensing probe. Grasping tasks utilize three sensing modalities of a soft gripper; finger pose, fingertip contact, and force in the palm all provide feedback for dexterous manipulation. We discuss and address in future work the effects of atmosphere and air flow rate on resonant frequency as well as limitations in signal processing of this sensor design.

Influence of Product Interface Material Stiffness on Human Tactile Perception during a Grasping Task

When considering product handle ergonomics, authors have focused on product handle sizes and shapes, while handle materials have been largely ignored. Authors have shown that handles coated with rubber foam were more comfortable than stiff handles. However, they did not provide detailed material properties, nor did they investigate different stiffnesses and their impact on tactile perception during grasping. Therefore, in this article, we investigated the influence of product interface material stiffness using a common wood sawing task with a saw handle made of hard plastic and 3D-printed deformable material with different stiffnesses. The results showed that user tactile perception can be improved significantly where the 3D-printed cellular density, and, hence material stiffness, has a significant influence on the resulting tactile perception. However, results have shown that the material stiffness must be determined appropriately to maintain the stability of the products in hands. The results also suggest that the product interface material had a greater influence on the reported overall comfort rating than the product handle shape in the sawing task.

A Lightweight Beak-Like Sensing System for Grasping Tasks of Flapping Aerial Robots

Many sensor systems in robotics are bio-inspired by similar mechanisms in living creatures. Birds frequently use their beaks to grasp and manipulate objects. This work proposes a very lightweight sensor system that emulates a bird’s beak, thus allowing flapping aerial robots to interact with the environment, as e.g. to perform grasping or manipulation tasks. The sensor system is composed of a flexible link (beam) actuated by a micro servomotor, two strain gauges placed on different points and a rigid link opposed. Additionally, a new algorithm is also developed that estimates the instant at which the beak impacts with an object, the contact position and the exerted force. Our sensor system outperforms the existing designs in robotics applications, because it is lightweight, small, cheap, with very low computational load and without any complementary perception. It is demonstrated that the adequate placement of two strain gauges allow the estimation of the contact point and the force exerted between the beam and an object, and the accuracy achieved is enough to reckon properties of the object and develop force control systems. The validation has been made and reported through finite-element simulations and experiments, and the results illustrate the efficiency of the prototype and the proposed algorithm.

Data-Driven Kinematic Model of PneuNets Bending Actuators for Soft Grasping Tasks

The paper proposes a novel data-driven approximation kinematic (DAK) model to estimate the shape and opening level of a PneuNets soft gripper in relation to the applied pressure signal. The model offers suitable capabilities for implementing in real-time applications involving soft grasping planning and size recognition of fragile objects with different sizes and shapes. The proposed DAK model estimates the free bending behavior of a PneuNets actuator (soft gripper finger) based on a set of approximation functions derived from experimental data and an equivalent serial mechanism that mimics the shape of the actuator. The model was tested for a commercial PneuNets actuator with decreasing chamber height, produced by SoftGripping Co. (Hamburg, Germany). The model validation is accomplished through a set of experiments, where the shape and elementary displacements were measured using a digital image processing technique. The experimental data and the estimated data from the DAK model were compared and analyzed, respectively. The proposed approach has applicability in sensorless/self-sensing bending control algorithms of PneuNets actuators and in soft grasping applications where the robotic system must estimate the opening level of the gripper in order to be able to accomplish its task.

Design and Characterization of a New Soft Fingertip Force Sensor for Precision Robotic Grasping Task

Nowadays, a new robotic field has emerged, called Soft Robotics, and it is a new field that involves new challenges, such as soft manipulation, considering external force measure or contact sensors. Thus, in this paper, a new soft fingertip force sensor is introduced. The methodology proposed consists in a quantitative applied research aiming to propose a device able to detect and measure external applied forces. In order to reach the above target, the following steps have been performed: i.) requirements identification, ii) transductor selection, ii) rigid structure design, iii.) soft structure design, iv.) definition of variables parameters for sensor characterization, v.) implementation of the experiments to quantify the relation between external forces and output voltage for different operation conditions, and vi.) proposition of the mathematical model. The mathematical model is defined to describe applied external force and sensor output voltage, demonstrating different mathematical models for two soft materials (polyaddition and polycondensation silicones). It has been found out that using a softer silicone increases the sensor sensitivity. However, the selection of silicone depends on the application requirements. Moreover, the use of soft materials gives a high compliance level with contact objects. On the other hand, a new sensor structure that could be implemented using a 3D print and a FSR traductor, easily implemented by a 3D Printer, has been proposed. Then the mathematical model presented could be used by researchers Moreover, researchers could modify the parameters explained in order to obtain different soft sensor performance. Thus, the soft sensor is endowed with versatility, compliance, and molding.

Influence of Visual and Haptic Feedback on the Detection of Threshold Forces in a Surgical Grasping Task

Feedback from sensory modalities is crucial for the precise grasping of tissues during minimally invasive robotic surgery. The aims of the study are to determine the influence of visual and haptic feedback on the detection of threshold forces and to evaluate the applicability of the sensory integration model to a surgical grasping task. A sensorized surgical grasper and a fingertip haptic force feedback device were used. Three types of stimuli were presented (i.e. visual-alone, haptic-alone, and bimodal visual and haptic stimuli). Threshold forces of 100 mN and 87.5 mN were detected for visual and haptic feedback, respectively. When bimodal feedback was provided, the participants detected a threshold force of 75 mN. The threshold force for the bimodal condition was 28.6% lower than the visual-alone feedback and 15.4% lower than the haptic-alone feedback stimuli. Our bimodal condition results showed that there was a 13.1% difference between the experimental result and the predicted value from the sensory integration model. The threshold force discrimination was strongly influenced by the haptic force feedback. It is likely that the tissue stiffness can be more intuitively perceived through the direct force stimulation of the fingertip than just by visual observation alone. Cues like small deformations or changes in the grasping angles of the surgical tool are more difficult to interpret visually as compared to the haptic modality.

Octopus‐Inspired Soft Arm with Suction Cups for Enhanced Grasping Tasks in Confined Environments

Octopus‐Inspired Soft Arm with Suction Cups for Enhanced Grasping Tasks in Confined Environments

Octopus‐Inspired Soft Arm with Suction Cups for Enhanced Grasping Tasks in Confined Environments

Octopuses have impressive manipulation capabilities empowered by the intrinsic adaptation of their soft arms combined with distributed suckers. Drawing inspiration from the arm morphology and abilities of the octopus, the integrated design of a conical soft robotic arm endowed with suction cups is presented herein. The proposed arm exploits soft robotic technologies as in materials (combining stiff and soft silicones) and actuation (tendons to drive motion and fluidic channels for suction cup actuation) for retrieving objects in confined spaces of unknown shapes and sizes. The soft arm is able to grasp varied and complex‐shaped objects, to move in air, water, and oil, and to retrieve items immersed in a 70 mm diameter pipe, under pressure (up to 18 bar). Suction cups enable the arm to retrieve objects that would be impossible to grasp without them and help in improving the grasping force up to ≈1.4 times in air, ≈2.4 times in water, and ≈12.5 times in oil. The enhanced holding force (up to 3.3 N, ≈3.9 times arm weight) and a wide spectrum of objects negotiable by the arm make it a universal tool for object retrieval in confined spaces and wet conditions.