Статті в журналах: "Grasping strategies"

1

Desanghere, L., and J. Marotta. "Gaze strategies and grasping: Complex shapes." Journal of Vision 9, no. 8 (March 21, 2010): 1108. http://dx.doi.org/10.1167/9.8.1108.

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2

Huang, Xiaoqian, Mohamad Halwani, Rajkumar Muthusamy, Abdulla Ayyad, Dewald Swart, Lakmal Seneviratne, Dongming Gan, and Yahya Zweiri. "Real-time grasping strategies using event camera." Journal of Intelligent Manufacturing 33, no. 2 (January 10, 2022): 593–615. http://dx.doi.org/10.1007/s10845-021-01887-9.

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AbstractRobotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. With advantages of microsecond-level sampling rate and no motion blur of event camera, the model-based and model-free approaches are developed for known and unknown objects’ grasping respectively. The event-based multi-view approach is used to localize the objects in the scene in the model-based approach, and then point cloud processing is utilized to cluster and register the objects. The proposed model-free approach, on the other hand, utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. Using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper, the proposed approaches are experimentally validated with objects of different sizes. Furthermore, it demonstrates robustness and a significant advantage over grasping with a traditional frame-based camera in low-light conditions.

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3

Peckre, Louise R., Anne-Claire Fabre, Julien Hambuckers, Christine E. Wall, lluís Socias-Martínez, and Emmanuelle Pouydebat. "Food properties influence grasping strategies in strepsirrhines." Biological Journal of the Linnean Society 127, no. 3 (February 15, 2019): 583–97. http://dx.doi.org/10.1093/biolinnean/bly215.

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4

Leferink, Charlotte, Hannah Stirton, and Jonathan Marotta. "Visuomotor strategies for grasping a rotating target." Journal of Vision 15, no. 12 (September 1, 2015): 1151. http://dx.doi.org/10.1167/15.12.1151.

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5

Bulloch, Melissa C., Steven L. Prime, and Jonathan J. Marotta. "Anticipatory gaze strategies when grasping moving objects." Experimental Brain Research 233, no. 12 (August 20, 2015): 3413–23. http://dx.doi.org/10.1007/s00221-015-4413-7.

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6

Roby-Brami, Agnès, Sylvie Fuchs, Mounir Mokhtari, and Bernard Bussel. "Reaching and Grasping Strategies in Hemiparetic Patients." Motor Control 1, no. 1 (January 1997): 72–91. http://dx.doi.org/10.1123/mcj.1.1.72.

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7

Hasegawa, Yasuhisa, Kensaku Kanada, and Toshio Fukuda. "Dexterous manipulation from pinching to power grasping - performance comparison of grasping strategies for different objects." IFAC Proceedings Volumes 36, no. 17 (September 2003): 335–40. http://dx.doi.org/10.1016/s1474-6670(17)33416-x.

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8

Dzitac, Pavel, and Md Mazid Abdul. "Modeling of an Object Manipulation Motion Planner and Grasping Rules." Applied Mechanics and Materials 278-280 (January 2013): 664–72. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.664.

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This paper presents the development of a Motion Planning Module for object manipulation, which is a part of previously developed robotic grasping and manipulation controller. The Motion Planning Module consists of a sensing processor, decision making module, instinctive controller, motion planner and a planned motion controller. Details related to the design and modelling of the motion planning module have been offered. Results of experiments on human grasping rule, suitable for the grasping and manipulation controller, have been discussed. The output of this research may be useful to those developing motion planning strategies for their grasping and manipulation controllers.

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9

Touillet, Amélie, Adrienne Gouzien, Marina Badin, Pierrick Herbe, Noël Martinet, Nathanaël Jarrassé, and Agnès Roby-Brami. "Kinematic analysis of impairments and compensatory motor behavior during prosthetic grasping in below-elbow amputees." PLOS ONE 17, no. 11 (November 18, 2022): e0277917. http://dx.doi.org/10.1371/journal.pone.0277917.

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After a major upper limb amputation, the use of myoelectric prosthesis as assistive devices is possible. However, these prostheses remain quite difficult to control for grasping and manipulation of daily life objects. The aim of the present observational case study is to document the kinematics of grasping in a group of 10 below-elbow amputated patients fitted with a myoelectric prosthesis in order to describe and better understand their compensatory strategies. They performed a grasping to lift task toward 3 objects (a mug, a cylinder and a cone) placed at two distances within the reaching area in front of the patients. The kinematics of the trunk and upper-limb on the non-amputated and prosthetic sides were recorded with 3 electromagnetic Polhemus sensors placed on the hand, the forearm (or the corresponding site on the prosthesis) and the ipsilateral acromion. The 3D position of the elbow joint and the shoulder and elbow angles were calculated thanks to a preliminary calibration of the sensor position. We examined first the effect of side, distance and objects with non-parametric statistics. Prosthetic grasping was characterized by severe temporo-spatial impairments consistent with previous clinical or kinematic observations. The grasping phase was prolonged and the reaching and grasping components uncoupled. The 3D hand displacement was symmetrical in average, but with some differences according to the objects. Compensatory strategies involved the trunk and the proximal part of the upper-limb, as shown by a greater 3D displacement of the elbow for close target and a greater forward displacement of the acromion, particularly for far targets. The hand orientation at the time of grasping showed marked side differences with a more frontal azimuth, and a more “thumb-up” roll. The variation of hand orientation with the object on the prosthetic side, suggested that the lack of finger and wrist mobility imposed some adaptation of hand pose relative to the object. The detailed kinematic analysis allows more insight into the mechanisms of the compensatory strategies that could be due to both increased distal or proximal kinematic constraints. A better knowledge of those compensatory strategies is important for the prevention of musculoskeletal disorders and the development of innovative prosthetics.

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10

Prime, S. L., and J. J. Marotta. "Gaze strategies during visually-guided and memory-guided grasping." Journal of Vision 11, no. 11 (September 23, 2011): 967. http://dx.doi.org/10.1167/11.11.967.

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11

Leferink, Charlotte, Neil Bruce, and Jonathan Marotta. "Visualization of viewing strategies for grasping a rotating target." Journal of Vision 17, no. 10 (August 31, 2017): 464. http://dx.doi.org/10.1167/17.10.464.

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12

Desanghere, L., and J. Marotta. "Gaze strategies while grasping: What are you looking at?!" Journal of Vision 8, no. 6 (March 20, 2010): 299. http://dx.doi.org/10.1167/8.6.299.

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13

Prime, Steven L., and Jonathan J. Marotta. "Gaze strategies during visually-guided versus memory-guided grasping." Experimental Brain Research 225, no. 2 (December 13, 2012): 291–305. http://dx.doi.org/10.1007/s00221-012-3358-3.

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14

McCarty, Michael, and Rachel Clifton. "Infant's and toddler's strategies for grasping and using tools." Infant Behavior and Development 21 (April 1998): 211. http://dx.doi.org/10.1016/s0163-6383(98)91426-6.

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15

Kyberd, Peter. "Slip Detection Strategies for Automatic Grasping in Prosthetic Hands." Sensors 23, no. 9 (April 30, 2023): 4433. http://dx.doi.org/10.3390/s23094433.

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The detection of an object slipping within the grasp of a prosthetic hand enables the hand to react to ensure the grasp is stable. The computer controller of a prosthetic hand needs to be able to unambiguously detect the slide from other signals. Slip can be detected from the surface vibrations made as the contact between object and terminal device shifts. A second method measures the changes in the normal and tangential forces between the object and the digits. After a review of the principles of how the signals are generated and the detection technologies are employed, this paper details the acoustic and force sensors used in versions of the Southampton Hand. Attention is given to the techniques used in the field. The performance of the Southampton tube sensor is explored. Different surfaces are slid past a sensor and the signals analysed. The resulting signals have low-frequency content. The signals are low pass filtered and the resulting processing results in a consistent response across a range of surfaces. These techniques are fast and not computationally intensive, which makes them practical for a device that is to be used daily in the field.

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16

GREENWALD, HAL S., and DAVID C. KNILL. "A comparison of visuomotor cue integration strategies for object placement and prehension." Visual Neuroscience 26, no. 1 (January 2009): 63–72. http://dx.doi.org/10.1017/s0952523808080668.

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AbstractVisual cue integration strategies are known to depend on cue reliability and how rapidly the visual system processes incoming information. We investigated whether these strategies also depend on differences in the information demands for different natural tasks. Using two common goal-oriented tasks, prehension and object placement, we determined whether monocular and binocular information influence estimates of three-dimensional (3D) orientation differently depending on task demands. Both tasks rely on accurate 3D orientation estimates, but 3D position is potentially more important for grasping. Subjects placed an object on or picked up a disc in a virtual environment. On some trials, the monocular cues (aspect ratio and texture compression) and binocular cues (e.g., binocular disparity) suggested slightly different 3D orientations for the disc; these conflicts either were present upon initial stimulus presentation or were introduced after movement initiation, which allowed us to quantify how information from the cues accumulated over time. We analyzed the time-varying orientations of subjects’ fingers in the grasping task and those of the object in the object placement task to quantify how different visual cues influenced motor control. In the first experiment, different subjects performed each task, and those performing the grasping task relied on binocular information more when orienting their hands than those performing the object placement task. When subjects in the second experiment performed both tasks in interleaved sessions, binocular cues were still more influential during grasping than object placement, and the different cue integration strategies observed for each task in isolation were maintained. In both experiments, the temporal analyses showed that subjects processed binocular information faster than monocular information, but task demands did not affect the time course of cue processing. How one uses visual cues for motor control depends on the task being performed, although how quickly the information is processed appears to be task invariant.

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17

Dong, Xiaoxiao, Chen Wang, Haoxin Song, Jinqiang Shao, Guiyao Lan, Jiaming Zhang, Xiangkun Li, and Ming Li. "Advancement in Soft Hydrogel Grippers: Comprehensive Insights into Materials, Fabrication Strategies, Grasping Mechanism, and Applications." Biomimetics 9, no. 10 (September 27, 2024): 585. http://dx.doi.org/10.3390/biomimetics9100585.

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Soft hydrogel grippers have attracted considerable attention due to their flexible/elastic bodies, stimuli-responsive grasping and releasing capacity, and novel applications in specific task fields. To create soft hydrogel grippers with robust grasping of various types of objects, high load capability, fast grab response, and long-time service life, researchers delve deeper into hydrogel materials, fabrication strategies, and underlying actuation mechanisms. This article provides a systematic overview of hydrogel materials used in soft grippers, focusing on materials composition, chemical functional groups, and characteristics and the strategies for integrating these responsive hydrogel materials into soft grippers, including one-step polymerization, additive manufacturing, and structural modification are reviewed in detail. Moreover, ongoing research about actuating mechanisms (e.g., thermal/electrical/magnetic/chemical) and grasping applications of soft hydrogel grippers is summarized. Some remaining challenges and future perspectives in soft hydrogel grippers are also provided. This work highlights the recent advances of soft hydrogel grippers, which provides useful insights into the development of the new generation of functional soft hydrogel grippers.

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18

Michalland, Arthur-Henri, Guillaume Thébault, Johan Briglia, Philippe Fraisse, and Denis Brouillet. "Grasping a Chestnut Burr." Experimental Psychology 66, no. 4 (July 2019): 310–17. http://dx.doi.org/10.1027/1618-3169/a000449.

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Abstract. This work aimed to assess the role of manual laterality in action coding strategies and, subsequently, in environmental features relevant for each hand’s action. Relying on Eder and Hommel’s (2013) proposal, we distinguished stimulus-related and end state-related consequences in a Simon paradigm where right-handed participants were divided into two groups, one responding with gloves and one without. Two objects were presented pictorially: one for which sensory consequences of grasping were negatively valenced (a chestnut burr), and one for which they were positively valenced (an apricot). By these means, stimulus and end-state effects could be assessed separately, along with the relevance of each feature of the experimental settings. Results showed that the use of one’s dominant or non dominant hand gives rise to different repercussions of stimulus-related and end state-related effects on response: Responses made with the right (dominant) hand were based on an elaborated coding (representing features of stimulus-related and end state-related consequences of action). In contrast, responses made with the left (non dominant) hand seemed to be based on a less elaborated coding (not taking into account end-state consequences of an action).

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19

Fontana, Gianmauro, Serena Ruggeri, Irene Fassi, and Giovanni Legnani. "A mini work-cell for handling and assembling microcomponents." Assembly Automation 34, no. 1 (January 28, 2014): 27–33. http://dx.doi.org/10.1108/aa-11-2012-087.

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Purpose – The purpose of this paper was the design, development, and test of a flexible and reconfigurable experimental setup for the automatic manipulation of microcomponents, enhanced by an accurately developed vision-based control. Design/methodology/approach – To achieve a flexible and reconfigurable system, an experimental setup based on 4 degrees of freedom robot and a two-camera vision system was designed. Vision-based strategies were adopted to suitably support the motion system in easily performing precise manipulation operations. A portable and flexible program, incorporating the machine vision module and the control module of the task operation, was developed. Non-conventional calibration strategies were also conceived for the complete calibration of the work-cell. The developed setup was tested and exploited in the execution of repetitive tests of the grasping and releasing of microcomponents, testing also different grasping and releasing strategies. Findings – The system showed its ability in automatically manipulating microcomponents with two different types of vacuum grippers. The performed tests evaluated the success and precision of the part grasping and release, which is a crucial aspect of micromanipulation. The results confirm reliability in grasping and that the release is precluded by adhesive effects. Thus, different strategies were adopted to improve the efficiency in the release of stuck components without negatively affecting the accuracy nor the repeatability of the positioning. Originality/value – This work provided a flexible and reconfigurable architecture devoted to the automatic manipulation of microcomponents, methodologies for the characterization of different vacuum microgrippers, and quantitative information about their performance, to date missing in literature.

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20

Cao, Chuqing, and Hanwei Liu. "Grasp Pose Detection Based on Shape Simplification." International Journal of Humanoid Robotics 18, no. 03 (June 2021): 2150006. http://dx.doi.org/10.1142/s0219843621500067.

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For robots in an unstructured work environment, grasping unknown objects that have neither model data nor RGB data is very important. The key to robotic autonomous grasping is not only in the judgment of object type but also in the shape of the object. We present a new grasping approach based on the basic compositions of objects. The simplification of complex objects is conducive to the description of object shape and provides effective ideas for the selection of grasping strategies. First, the depth camera is used to obtain partial 3D data of the target object. Then the 3D data are segmented and the segmented parts are simplified to a cylinder, a sphere, an ellipsoid, and a parallelepiped according to the geometric and semantic shape characteristics. The grasp pose is constrained according to the simplified shape feature and the core part of the object is used for grasping training using deep learning. The grasping model was evaluated in a simulation experiment and robot experiment, and the experiment result shows that learned grasp score using simplified constraints is more robust to gripper pose uncertainty than without simplified constraint.

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21

Osa, T., Jan Peters, and G. Neumann. "Hierarchical reinforcement learning of multiple grasping strategies with human instructions." Advanced Robotics 32, no. 18 (September 7, 2018): 955–68. http://dx.doi.org/10.1080/01691864.2018.1509018.

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22

Chen, Dong, Ziyuan Liu, and Georg von Wichert. "Uncertainty-Aware Arm-Base Coordinated Grasping Strategies for Mobile Manipulation." Journal of Intelligent & Robotic Systems 80, S1 (May 5, 2015): 205–23. http://dx.doi.org/10.1007/s10846-015-0234-y.

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23

Christopoulos, Vassilios N., and Paul R. Schrater. "An Optimal Feedback Control Framework for Grasping Objects with Position Uncertainty." Neural Computation 23, no. 10 (October 2011): 2511–36. http://dx.doi.org/10.1162/neco_a_00180.

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As we move, the relative location between our hands and objects changes in uncertain ways due to noisy motor commands and imprecise and ambiguous sensory information. The impressive capabilities humans display for interacting and manipulating objects with position uncertainty suggest that our brain maintains representations of location uncertainty and builds compensation for uncertainty into its motor control strategies. Our previous work demonstrated that specific control strategies are used to compensate for location uncertainty. However, it is an open question whether compensation for position uncertainty in grasping is consistent with the stochastic optimal feedback control, mainly due to the difficulty of modeling natural tasks within this framework. In this study, we develop a stochastic optimal feedback control model to evaluate the optimality of human grasping strategies. We investigate the properties of the model through a series of simulation experiments and show that it explains key aspects of previously observed compensation strategies. It also provides a basis for individual differences in terms of differential control costs—the controller compensates only to the extent that performance benefits in terms of making stable grasps outweigh the additional control costs of compensation. These results suggest that stochastic optimal feedback control can be used to understand uncertainty compensation in complex natural tasks like grasping.

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24

Hu, Jie, Qin Li, and Qiang Bai. "Research on Robot Grasping Based on Deep Learning for Real-Life Scenarios." Micromachines 14, no. 7 (July 8, 2023): 1392. http://dx.doi.org/10.3390/mi14071392.

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The application of robots, especially robotic arms, has been primarily focused on the industrial sector due to their relatively low level of intelligence. However, the rapid development of deep learning has provided a powerful tool for conducting research on highly intelligent robots, thereby offering tremendous potential for the application of robotic arms in daily life scenarios. This paper investigates multi-object grasping in real-life scenarios. We first analyzed and improved the structural advantages and disadvantages of convolutional neural networks and residual networks from a theoretical perspective. We then constructed a hybrid grasping strategy prediction model, combining both networks for predicting multi-object grasping strategies. Finally, we deployed the trained model in the robot control system to validate its performance. The results demonstrate that both the model prediction accuracy and the success rate of robot grasping achieved by this study are leading in terms of performance.

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25

Bensmail, Djamel, Johanna Robertson, Christophe Fermanian, and Agnès Roby-Brami. "Botulinum Toxin to Treat Upper-Limb Spasticity in Hemiparetic Patients: Grasp Strategies and Kinematics of Reach-to-Grasp Movements." Neurorehabilitation and Neural Repair 24, no. 2 (September 28, 2009): 141–51. http://dx.doi.org/10.1177/1545968309347683.

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Background. Poor control of grasping in spastic, hemiparetic patients could be because of a combination of poor individuation of joints, weakness, spasticity, and sensory loss. Objective. To investigate the effect of botulinum toxin injections (BTIs) on grasping objects of different shapes and to assess the effect on upper-limb function, reach-to-grasp kinematics, and hand position and orientation at the time of grasp. Methods. We included 15 patients with spastic hemiparesis and 9 healthy controls in this open labeled study, in which the patients were assessed before (M0), 1 month after a first (M1), and 1 month after a second BTI (M4, at 4 months). A motion capture system recorded movements. Kinematic variables were computed as well as hand position and orientation at the time of grasping, and finger configurations were coded from video recordings. Results. In contrast with healthy participants, hemiparetic patients rarely used multipulpar grasps but used specific strategies combined with various directions of approach to the object. BTIs did not alter finger configuration but improved the final direction of the approach and the hand posture during the grasp. No significant changes in kinematic parameters were found using post hoc analysis, although a session effect was found for peak hand velocity. Individual analysis showed that the patients with the best potential for functional improvement are those with good proximal and moderate distal motor command. Conclusions. BTIs can modify hand kinematics as well as the approach and posture of reach-to-grasp movements. Function and grasping strategies are probably more dependent on motor recovery.

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26

Ji, Yi Guo, Zhi Huan Lan, Cui Chen, Chun Yan Tian, and Dong Yang. "The Study on Strategies of Winning Information Dominance in Modern Battlefield." Advanced Materials Research 926-930 (May 2014): 2194–97. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2194.

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The recent local wars appeared a lot of information characteristics. In future wars, one who master information dominance in battlefield will become more active. In a sense, one who controls the information in the battlefield will grasp the whole initiatives in the war. In the paper, we have researched the methods of grasping the information dominance.

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27

Prime, Steven L., and Jonathan J. Marotta. "Erratum to: Gaze strategies during visually-guided versus memory-guided grasping." Experimental Brain Research 225, no. 2 (February 13, 2013): 307. http://dx.doi.org/10.1007/s00221-013-3432-5.

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28

Hu, Yingbai, Xinyu Wu, Peng Geng, and Zhijun Li. "Evolution Strategies Learning With Variable Impedance Control for Grasping Under Uncertainty." IEEE Transactions on Industrial Electronics 66, no. 10 (October 2019): 7788–99. http://dx.doi.org/10.1109/tie.2018.2884240.

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29

Teich, Paul F. "Are Lawyers Truly Greedy? An Analysis of Relevant Empirical Evidence." Texas Wesleyan Law Review 19, no. 4 (March 2013): 837–97. http://dx.doi.org/10.37419/twlr.v19.i4.1.

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This Article addresses a single question: Have lawyers exploited the increase in demand for their services by adopting increasingly grasping practices such as price gauging, overbilling, and aggressive collection strategies?

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30

Li, Shaobo, Qiang Bai, Jing Yang, and Liya Yu. "Research on Grasping Strategy Based on Residual Network." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012066. http://dx.doi.org/10.1088/1742-6596/2203/1/012066.

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Abstract At present, the research on rectangular grasp strategy is generally based on object detection algorithm, which limits the improvement of model accuracy and generalization performance. This paper studies the semantic segmentation model based on residual network, and uses it to generate grasp strategies. The improved algorithm model not only achieves excellent rectangular grasping strategy prediction, but also has good generalization performance.

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31

Ma, Yanqin, Kai Du, Dongfeng Zhou, Juan Zhang, Xilong Liu, and De Xu. "Automatic precision robot assembly system with microscopic vision and force sensor." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141985161. http://dx.doi.org/10.1177/1729881419851619.

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An automatic precision robot assembly system is established. The robot assembly system mainly consists of an industrial robot, three cameras, a micro force sensor, and a specific gripper. The industrial robot is a six-axis serial manipulator, which is used to conduct grasping and assembly subtasks. Two microscopic cameras are fixed on two high accuracy translational platforms to provide visual information in aligning stage for assembly. While one conventional camera is installed on the robotic end effector to guide the gripper to grasp component. The micro force sensor is installed on the robotic end effector to perceive the contacted forces in inserting stage. According to the characteristics of components, an adsorptive gripper is designed to pick up components. In addition, a three-stage “aligning–approaching–grasping” control strategy for grasping subtask and a two-stage “aligning–inserting” control strategy for assembly subtask are proposed. Position offset compensation is computed and introduced into aligning stage for assembly to make the grasped component in the microscopic cameras’ small field of view. Finally, based on the established robot assembly system and the proposed control strategies, the assembly tasks including grasping and assembly are carried out automatically. With 30 grasping experiments, the success rate is 100%. Besides, the position and orientation alignment errors of pose alignment for assembly are less than 20 μm and 0.1°.

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32

Chen, Nutan, Keng Peng Tee, and Chee-Meng Chew. "Teleoperation grasp assistance using infra-red sensor array." Robotica 33, no. 4 (March 24, 2014): 986–1002. http://dx.doi.org/10.1017/s0263574714000733.

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SUMMARYTeleoperated grasping requires the abilities to follow the intended trajectory from the user and autonomously search for a suitable pre-grasp pose relative to the object of interest. Challenges include dealing with uncertainty due to the noise of the teleoperator, human elements and calibration errors in the sensors. To address these challenges, an effective and robust algorithm is introduced to assist grasping during teleoperation. Although without premature object contact or regrasping strategies, the algorithm enables the robot to perform online adjustments to reach a pre-grasp pose before final grasping. We use three infrared (IR) sensors that are mounted on the robot hand, and design an algorithm that controls the robot hand to grasp objects using the information from the sensors' readings and the interface component. Finally, a series of experiments demonstrate that the system is robust when grasping a wide range of objects and tracking slow-moving mobile objects. Empirical data from a five-subject user study allows us to tune the relative contributions from the IR sensors and the interface component so as to achieve a balance of grasp assistance and teleoperation.

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33

Cong, Qingzheng, Wen Fan, and Dandan Zhang. "TacFR-Gripper: A Reconfigurable Fin-Ray-Based Gripper with Tactile Skin for In-Hand Manipulation." Actuators 13, no. 12 (December 17, 2024): 521. https://doi.org/10.3390/act13120521.

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This paper introduces the TacFR-Gripper, a novel reconfigurable soft robotic gripper inspired by the Fin-Ray effect and equipped with tactile skin. The gripper incorporates a four-bar mechanism for accurate finger bending and a reconfigurable design to change the relative positions between the fingers and palm, enabling precise and adaptable object grasping. This 5-Degree-of-Freedom (DOF) soft gripper can facilitate dexterous manipulation of objects with diverse shapes and stiffness and is beneficial to the safe and efficient grasping of delicate objects. An array of Force Sensitive Resistor (FSR) sensors is embedded within each robotic fingertip to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. Moreover, we implemented a threshold-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. To verify the effectiveness of the TacFR-Gripper, we provide detailed workspace analysis to evaluate its grasping performance and conducted three experiments, including (i) assessing the grasp success rate across various everyday objects through different finger configurations, (ii) verifying the effectiveness of tactile skin with different control strategies in grasping, and (iii) evaluating the in-hand manipulation capabilities through object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin is demonstrated to enhance grasp stability by incorporating tactile feedback during manipulations.

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34

Rong, Jiacheng, Pengbo Wang, Tianjian Wang, Ling Hu, and Ting Yuan. "Fruit pose recognition and directional orderly grasping strategies for tomato harvesting robots." Computers and Electronics in Agriculture 202 (November 2022): 107430. http://dx.doi.org/10.1016/j.compag.2022.107430.

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Vitrani, Giuseppe, Simone Cortinovis, Luca Fiorio, Marco Maggiali, and Rocco Antonio Romeo. "Improving the Grasping Force Behavior of a Robotic Gripper: Model, Simulations, and Experiments." Robotics 12, no. 6 (October 31, 2023): 148. http://dx.doi.org/10.3390/robotics12060148.

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Robotic grippers allow industrial robots to interact with the surrounding environment. However, control architectures of the grasping force are still rare in common industrial grippers. In this context, one or more sensors (e.g., force or torque sensors) are necessary. However, the incorporation of such sensors might heavily affect the cost of the gripper, regardless of its type (e.g., pneumatic or electric). An alternative approach could be open-loop force control strategies. Hence, this work proposes an approach for optimizing the open-loop grasping force behavior of a robotic gripper. For this purpose, a specialized robotic gripper was built, as well as its mathematical model. The model was employed to predict the gripper performance during both static and dynamic force characterization, simulating grasping tasks under different experimental conditions. Both simulated and experimental results showed that by managing the mechanical properties of the finger–object contact interface (e.g., stiffness), the steady-state force variability could be greatly reduced, as well as undesired effects such as finger bouncing. Further, the object’s size is not required unlike most of the grasping approaches for industrial rigid grippers, which often involve high finger velocities. These results may pave the way toward conceiving cheaper and more reliable open-loop force control techniques for use in robotic grippers.

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Donepudi, Praveen Kumar. "Reinforcement Learning for Robotic Grasping and Manipulation: A Review." Asia Pacific Journal of Energy and Environment 7, no. 2 (July 30, 2020): 69–78. http://dx.doi.org/10.18034/apjee.v7i2.526.

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A century of robots is the 21st century. The robots have long been able to cross the divide between the virtual universe and the real world. Robotics, as the most successful contender in the upcoming great technological revolution, will play an ever more important role in society because of the impact it has in every field of life, including medicine, healthcare, architecture, manufacturing and food supplies, logistics and transport. This document introduces a modern approach to the grasp of robots, which draws grasp techniques from the human demonstration and combines these strategies into a grasp-planning framework, in order to produce a viable insight into the objective geometry and manipulation tasks of the object. Our study findings show that grasping strategies of the form of grasp and thumbs positioning are not only necessary for human grasp but also significant restrictions on posture and wrist posture which greatly reduce both the robot hand's workplace and the search space for grasp planning. In the simulation and with a true robotic system this method has been extensively tested for several everyday living representative objects. In the experiment with varying degrees of perceiving in certainties, we have demonstrated the power of our method.

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Guo, Hongshuang, Hao Zeng, and Arri Priimagi. "Optically controlled grasping-slipping robot moving on tubular surfaces." Multifunctional Materials 5, no. 2 (March 29, 2022): 024001. http://dx.doi.org/10.1088/2399-7532/ac55fd.

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Abstract Stimuli-responsive polymers provide unmatched opportunities for remotely controlled soft robots navigating in complex environments. Many of the responsive-material-based soft robots can walk on open surfaces, with movement directionality dictated by the friction anisotropy at the robot-substrate interface. Translocation in one-dimensional space such as on a tubular surface is much more challenging due to the lack of efficient friction control strategies. Such strategies could in long term provide novel application prospects in, e.g. overhaul at high altitudes and robotic operation within confined environments. In this work, we realize a liquid-crystal-elastomer-based soft robot that can move on a tubular surface through optical control over the grasping force exerted on the surface. Photoactuation allows for remotely switched gripping and friction control which, together with cyclic body deformation, enables light-fueled climbing on tubular surfaces of glass, wood, metal, and plastic with various cross-sections. We demonstrate vertical climbing, moving obstacles along the path, and load-carrying ability (at least 3 × body weight). We believe our design offer new prospects for wirelessly driven soft micro-robotics in confined spacing.

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Shukla, Ashwini, and Aude Billard. "Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies." Robotics and Autonomous Systems 60, no. 3 (March 2012): 424–40. http://dx.doi.org/10.1016/j.robot.2011.07.023.

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Jarrassé, N., S. Martin, and A. Roby-Brami. "Instrumented objects for the study and quantitative evaluation of grasping and manipulation strategies." Annals of Physical and Rehabilitation Medicine 57 (May 2014): e179-e180. http://dx.doi.org/10.1016/j.rehab.2014.03.651.

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Van Waelvelde, Hilde, Willy De Weerdt, Paul De Cock, Bouwien C. M. Smits-Engelsman, and Wim Peersman. "Ball Catching Performance in Children with Developmental Coordination Disorder." Adapted Physical Activity Quarterly 21, no. 4 (October 2004): 348–63. http://dx.doi.org/10.1123/apaq.21.4.348.

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The aim of this study was to compare the quality of ball catching performance of children with DCD to the performance of younger typically developing children. The outcome measures used were a modified ball catching item of the Test of Gross Motor Development and the number of grasping errors in a ball catching test. In the study, children with DCD were matched with younger typically developing children according to gender and the number of caught balls in the ball catching test. Children with DCD made significantly more grasping errors and scored significantly lower on the modified TGMD-item. Children with DCD were not only delayed in ball catching but they also seemed to use different movement strategies compared to younger typically developing children.

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Huang, Chih-Yung, Guan-Wen Su, Yu-Hsiang Shao, Ying-Chung Wang, and Shang-Kuo Yang. "Rapid-Learning Collaborative Pushing and Grasping via Deep Reinforcement Learning and Image Masking." Applied Sciences 14, no. 19 (October 6, 2024): 9018. http://dx.doi.org/10.3390/app14199018.

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When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

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Pozzi, Maria, Sara Marullo, Gionata Salvietti, Joao Bimbo, Monica Malvezzi, and Domenico Prattichizzo. "Hand closure model for planning top grasps with soft robotic hands." International Journal of Robotics Research 39, no. 14 (August 10, 2020): 1706–23. http://dx.doi.org/10.1177/0278364920947469.

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Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

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Seo, N. J., and T. J. Armstrong. "Effect of elliptic handle shape on grasping strategies, grip force distribution, and twisting ability." Ergonomics 54, no. 10 (October 2011): 961–70. http://dx.doi.org/10.1080/00140139.2011.606923.

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Christopoulos, V., and P. Schrater. "Identifying strategies for grasping objects with position uncertainty using empirical cost-to-go functions." Journal of Vision 8, no. 6 (March 20, 2010): 296. http://dx.doi.org/10.1167/8.6.296.

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Metz, Stephen, Brian Isle, Sandra Dermo, and James Odom. "Thermostats for Individuals with Movement Disabilities: Design Options and Manipulation Strategies." Proceedings of the Human Factors Society Annual Meeting 36, no. 2 (October 1992): 180–84. http://dx.doi.org/10.1177/154193129203600209.

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Using common household products is often difficult for people with neuromuscular disorders, spinal cord injury, or arthritis. We need to better understand their capabilities when designing and adapting products that are easier for them to use. In this study, individuals with movement impairments used two experimental home control thermostats with features that allowed easier positioning and viewing. The participants employed a variety of grasping and manipulation strategies, including some that were not anticipated by the designers. Participants' preferences indicated that the appearance of the product, not just effective control design, was an important factor in their judgments. We discuss the implications of the study results for universal design and adaptation of traditional products for the elderly and those with disabilities.

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Hakem, Hadia. "Vocabulary learning strategies for vocabulary learning in literary texts." Global Journal of Foreign Language Teaching 12, no. 4 (November 29, 2022): 177–83. http://dx.doi.org/10.18844/gjflt.v12i4.6425.

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Literary texts are made up of lexis which enriches the beauty of scenes described or narrated. This makes them overloaded with vocabulary, sometimes, even daunting learners, particularly foreign language learners. Teachers of literature in an EFL classroom need to consider the difficulty of grasping large amounts of new words. To this end, it is necessary to teach the learners ways of learning vocabulary, by introducing vocabulary learning strategies to aid learners' grasp and retaining. This study aims to describe the application of Vocabulary Learning Strategy (VLS) in an EFL classroom and more precisely with a literary course. In this descriptive paper, a literature review on vocabulary learning strategies will be presented introducing their taxonomies and their importance in an EFL context. In addition, a framework for training learners to use these strategies will be described concerning a short story. Keywords: literary texts; memory strategies; vocabulary; vocabulary learning strategies.

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Xu, Zeyu, Wenbo Shi, Dianbo Zhao, Ke Li, Junguang Li, Junyi Dong, Yu Han, Jiansheng Zhao, and Yanhong Bai. "Research Progress on Low Damage Grasping of Fruit, Vegetable and Meat Raw Materials." Foods 12, no. 18 (September 15, 2023): 3451. http://dx.doi.org/10.3390/foods12183451.

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The sorting and processing of food raw materials is an important step in the food production process, and the quality of the sorting operation can directly or indirectly affect the quality of the product. In order to improve production efficiency and reduce damage to food raw materials, some food production enterprises currently use robots for sorting operations of food raw materials. In the process of robot grasping, some food raw materials such as fruits, vegetables and meat have a soft appearance, complex and changeable shape, and are easily damaged by the robot gripper. Therefore, higher requirements have been put forward for robot grippers, and the research and development of robot grippers that can reduce damage to food raw materials and ensure stable grasping has been a major focus. In addition, in order to grasp food raw materials with various shapes and sizes with low damage, a variety of sensors and control strategies are required. Based on this, this paper summarizes the low damage grasp principle and characteristics of electric grippers, pneumatic grippers, vacuum grippers and magnetic grippers used in automated sorting production lines of fruit, vegetable and meat products, as well as gripper design methods to reduce grasp damage. Then, a grasping control strategy based on visual sensors and tactile sensors was introduced. Finally, the challenges and potential future trends faced by food robot grippers were summarized.

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Soppelsa, Julie, Emmanuelle Pouydebat, Maëlle Lefeuvre, Baptiste Mulot, Céline Houssin, and Raphaël Cornette. "The relationship between distal trunk morphology and object grasping in the African savannah elephant (Loxodonta africana)." PeerJ 10 (March 28, 2022): e13108. http://dx.doi.org/10.7717/peerj.13108.

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Background During reach-to-grasp movements, the human hand is preshaped depending on the properties of the object. Preshaping may result from learning, morphology, or motor control variability and can confer a selective advantage on that individual or species. This preshaping ability is known in several mammals (i.e., primates, carnivores and rodents). However, apart from the tongue preshaping of lizards and chameleons, little is known about preshaping of other grasping appendages. In particular, the elephant trunk, a muscular hydrostat, has impressive grasping skills and thus is commonly called a hand. Data on elephant trunk grasping strategies are scarce, and nothing is known about whether elephants preshape their trunk tip according to the properties of their food. Methods To determine the influence of food sizes and shapes on the form of the trunk tip, we investigated the morphology of the distal part of the trunk during grasping movements. The influence of food item form on trunk tip shape was quantified in six female African savannah elephants (Loxodonta africana). Three food item types were presented to the elephants (elongated, flat, and cubic), as well as three different sizes of cubic items. A total of 107 ± 10 grips per individual were video recorded, and the related trunk tip shapes were recorded with a 2D geometric morphometric approach. Results Half of the individuals adjusted the shape of the distal part of their trunk according to the object type. Of the three elephants that did not preshape their trunk tip, one was blind and another was subadult. Discussion and perspectives We found that elephants preshaped their trunk tip, similar to the preshaping of other species’ hands or paws during reach-to-grasp movements. This preshaping may be influenced by visual feedback and individual learning. To confirm these results, this study could be replicated with a larger sample of elephants.

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Wang, Shuo, Jingjing Zheng, Bin Zheng, and Xianta Jiang. "Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG." Biosensors 12, no. 2 (January 21, 2022): 57. http://dx.doi.org/10.3390/bios12020057.

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Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a sweet period before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.

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Kangur, Karina, Jutta Billino, and Constanze Hesse. "Keeping Safe: Intra-individual Consistency in Obstacle Avoidance Behaviour Across Grasping and Locomotion Tasks." i-Perception 8, no. 1 (January 2017): 204166951769041. http://dx.doi.org/10.1177/2041669517690412.

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Successful obstacle avoidance requires a close coordination of the visual and the motor systems. Visual information is essential for adjusting movements in order to avoid unwanted collisions. Yet, established obstacle avoidance paradigms have typically either focused on gaze strategies or on motor adjustments. Here we were interested in whether humans show similar visuomotor sensitivity to obstacles when gaze and motor behaviour are measured across different obstacle avoidance tasks. To this end, we measured participants’ hand movement paths when grasping targets in the presence of obstacles as well as their gaze behaviour when walking through a cluttered hallway. We found that participants who showed more pronounced motor adjustments during grasping also spent more time looking at obstacles during locomotion. Furthermore, movement durations correlated positively in both tasks. Results suggest considerable intra-individual consistency in the strength of the avoidance response across different visuomotor measures potentially indicating an individual’s tendency to perform safe actions.

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