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135 results for “Grasping”
Fig. 1 in Chaetognath grasping spines from the Upper Mississippian of Arkansas (USA)
Fig. 1. Eoserratosagitta serrata gen. et sp. nov., Cove Creek (Arkansas, USA), Middle Chesterian (Namurian Aequivalent). A. Aconcretion fragment showing part of the accumulation of spines (s), scale bar 0.1 mm. B. A fragment of the middle part of a spine showing the concretion filled central cavity (cc), a row of bullet−like, hollow (now concretion−filled) denticles (d) on the concave side (to the left), and five longitudinal bands (b) separated by fine ribs (r) on the outer surface of the spine wall, scale bar 0.1 mm. C. Close−up of B showing fiber (f) impressions, scale bar 0.01 mm. All SEM micrographs.
Retrievals of aerosol optical, componential, and radiative properties from joint observations of sun photometer and Lidar using GRASP algorithm
<p>The site location is <span>114°21′E, 30°32′N (Central China). The time period is from 2021.07 to 2022.08.</span></p> <p><span>Data includes AOD (all sequences), SSA, ASY, ASD, CRI (only retrieved from sky irradiance), components (black carbon, brown carbon, dust, iron oxide, water-soluble inorganic salt and water), and vertical profiles of shapes of total extinction, fine-mode extinction, and coarse-mode extinction.</span></p>
SYNTHETIC dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"
<p>SYNTHETIC dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.</p> <p>In order to fully reproduce the experiments, download also the REAL dataset. </p> <p>To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.</p> <p>Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments</p>
REAL dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"
<p>REAL dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.</p> <p>In order to fully reproduce the experiments, download also the SYNTHETIC dataset. </p> <p>To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.</p> <p>Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments</p>
GP-net: Grasp Proposal for Mobile Manipulators
<p>This dataset includes all trained models and data to use and replicate our work for GP-net (under review for ICRA 2023).<br> <br> The pre-trained model for using GP-net on a PAL TIAGo mobile manipulator with a parallel jaw gripper is available in GP-net_model_pretrained.zip. It can be used with our <a href="https://github.com/AuCoRoboticsMU/gpnet-ros">gpnet-ros</a> repository.<br> <br> If you want to use our dataset and train an alternative network architecture from scratch, use GP-net_training_data.zip. You can use it with the <a href="https://github.com/AuCoRoboticsMU/GP-net">GP-net</a> repository to train models. If you want to run simulation experiments with your trained model, download GP-net_simulation_data.zip for the URDFs of the test objects and pal gripper.<br> <br> If you want to generate a new dataset for an alternative gripper, you can use the <a href="https://github.com/AuCoRoboticsMU/gpnet-data">gpnet-data</a> repository, which is based on DexNet2.0. The docker image to run the code is available here under gpnet-data_docker_image.tar</p> <p> </p>
Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video
<p>Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey.</p> <p>1. Video Files</p> <p>MarmoReach 1 is an illustrative example.</p> <p>MarmoReach 2 illustrates the reaching trial shown in Figure 3D.</p> <p>MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2. </p> <p>2. Data</p> <p>marmo_reach_model.mat is a Matlab struct.</p> <p>2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4. </p> <p>x.hand,y.hand = position data of the central hand marker for each trial.</p> <p>x.cricket,y.cricket = position data of the cricket marker for each trial.</p> <p>x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses. </p> <p>To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].</p> <p> </p>
Reaching and grasping objects in depth for people with stereovision deficits and healthy controls.
<p>Decades of research into hand-object interaction and manipulation skills has yielded fundamental insights with applications in robotics and motor learning. Nevertheless, integrating visual function (especially binocular function, important to perceive depth) into this equation is crucial, forming a triangle between vision, reaching, and object manipulation.</p> <p> </p> <p>The ReGraD dataset provides kinematic data during hand-object interaction in monocular and binocular conditions at different depths and monocular/binocular conditions. It comprises two sub-datasets: ReGraD A (two measurements) can determine its test-retest reliability, whilst ReGraD B (one measurement) can characterize individuals with and without visual disorders. ReGraD includes 35 controls and 3 patients with amblyopia aged 6 to 35.</p> <p> </p> <p>The ReGraD dataset may aid to (1) gain insights into hand-object interaction under various eye conditions and depths, (2) assess reliability and reproducibility and (3) examine the effects of groups (control vs. patients) and age, among others. The ReGraD dataset contains raw data that can also be used to develop algorithms for data segmentation and data interpolation in the kinematic field.</p>
Cognitive-based Prosthetics to Improve Grasp and Reaching After SCI
ClinicalTrials.gov study NCT04577573. IPD Sharing: YES. Countries: 1. Publications: 1.
Concurrent anticipation of two object dimensions during grasping in 10-month-old infants: A quantitative analysis
<p>Data set of Concurrent anticipation of two object dimensions during grasping in 10-month-old infants: A quantitative analysis</p>
The development of the grasp-height effect as a measure of efficient action planning in children.
<p>Data set of Jovanovic, B. & Schwarzer, G. (2017). The development of the grasp-height effect as a measure of efficient action planning in children. <em>Journal of Experimental Child Psychology, 153, </em>74-82.</p>
Griffin Grasping Nemesis' Wheel of Fate
"The Roman goddess of fate and revenge, Nemesis, was the personification of godly punishment for those guilty of pride. She was first portrayed as a female figure beside the wheel of fate and a griffin, a horned, winged creature with a beak and the body of a lion. Over time, however, she lost her anthropomorphic character and these two images came to represent the goddess in and of themselves. During the Roman Period, Nemesis rose in popularity- particularly among soldiers, who brought her cult to the Land. The base of the sculpture bears a greek inscription that reads "Year 522, I, Mercurios, son of Alexander, dedicate [this statue[ during my priesthood."" Photographed in the Israel Museum Erez, Northern Negev 210-211 CE Marable Source: Objaverse 1.0 / Sketchfab
IntelliMan_WP5_Grasping, Manipulationand Arm-Hand Coordination_T5.4_Experience-and Model-Based Grasp Synthesis and Manipulation_Pushing_v0
<p>The dataset provides the data recorded during the experiments described in the paper “Costanzo, M.; De Simone, M.; Federico, S.; Natale, C. Non-Prehensile Manipulation Actions and Visual 6D Pose Estimation for Fruit Grasping Based on Tactile Sensing. Robotics 2023, 12, 92. https://doi.org/10.3390/robotics12040092”</p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_DataFusion and Sensing Technology_characterization of sensing system for grippers_v0
<p>The dataset contains data related to the simulations and experiments presented in the publication:<br>G. Laudante, O. Pennacchio, and S. Pirozzi, “Multiphysics simulation for the optimization of an optoelectronic-based tactile sensor,” in Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 2023, pp. 101–110. (DOI: 10.5220/0012166900003543)</p>
Placement Aware Grasp Planning for Efficient Sequential Manipulation
<p>This is a summary and real-world experimental video of Placement Aware Grasp Planning for Efficient Sequential Manipulation. </p> <p>This work accepted in ECAI 2024. </p> <p>Paper link : <a href="https://ebooks.iospress.nl/doi/10.3233/FAIA241020">IOS Press Ebooks - Placement Aware Grasp Planning for Efficient Sequential Manipulation</a></p>
JARVIS-MoCap monkey grasping recordings and annotations
<p>12 camera synchronized recording of a rhesus macaque grasping different objects and fruit with his left hand. Included in the dataset are checkerboard calibration recordings, one 12 camera recording and a trainingset containing roughly 3000 manual annotations of 23 hand keypoints.</p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_sensing system design for grippers_v0
<p><span>The dataset includes pr</span><span>ecisely made Computer-Aided Design (CAD) models in .step format, representing essential components such as metallic frame, silicone pad, plastic case, plastic grid, and tactile board with integrated proximity sensor. Additionally, the dataset provides a complete assembly of multi-modal sensor model in .f3z format.</span></p> <p><span>Furthermore, the dataset includes essential design files for the electronic infrastructure, including a precisely engineered circuit schematic design file in .sch format and a printed circuit board layout design file in .brd format. Additionally, the dataset offers visual aids in the form of digital images (.png) showcasing top view, PCB model, ToF module, and sensor assembly configuration. These resources enable researchers to leverage the dataset's comprehensive capabilities for advanced investigations and practical implementations in sensor technology and design.</span></p>
Löhr-Limpens, M., Göhringer, F., Schenk, T., & Hesse, C. (2019). 'Grasping and perception are both affected by irrelevant information and secondary tasks: new evidence from the Garner paradigm'. Psychological Research. DOI: 10.1007/s00426-019-01151-z
<p>This repository contains 6 data files and one readme file describing the content of the data files.</p> <p> </p>
See & Grasp 2 Data Set
<p>This is the second See & Grasp data set introduced from Yildirim & Jacobs (2013). It is a data set containing both the visual and haptic features for "Fribbles". The set of "Fribbles" is larger than the original See & Grasp dataset, which is available <a href="http://www2.bcs.rochester.edu/sites/jacobslab/dataset.html">here</a>.</p> <p>Fribbles are complex, 3-D objects with multiple parts and spatial relations among the parts. Moreover, Fribbles have a categorical structure—that is, each Fribble is an exemplar from a category formed by perturbing a category prototype. There are 4 Fribble families, each with 4 species. Each Fribble has 4 slots, and one of 3 possible parts are attached to each slot, leading to (4 × (3<sup>4</sup>)) = 324 total Fribbles in each family. The unmodified 3-D object files for the whole set of Fribbles can be found on Mike Tarr's (Department of Psychology, Carnegie Mellon University) web pages (<a href="http://tarrlabwiki.cnbc.cmu.edu/index.php/Main_Page">TarrLab webpage</a>).</p> <p>The See & Grasp data set contains 891 items corresponding to 891 Fribbles (324 for the A and C families, and 243 for family B because the 3D model files for one of 4 species in family B seem to be corrupt). There are 3 entries associated with each item. One entry is the 3-D object model for a Fribble. The second entry is an image of a Fribble rendered from a canonical viewpoint so that the Fribble's parts and spatial relations among the parts are clearly visible. (Using the 3-D object model, users can easily generate new images of a Fribble from any desired viewpoint.) The third entry is a way of representing a Fribble's haptic features. It is a set of joint angles obtained from a grasp simulator known as "GraspIt!" (Miller & Allen, 2004). GraspIt! contains a simulator of a human hand. When forming the representation of a Fribble's haptic features, the input to GraspIt! was the 3-D object model for the Fribble. Its output was a set of 16 joint angles of the fingers of a simulated human hand obtained when the simulated hand "grasped" the Fribble. Grasps—or closings of the fingers around a Fribble—were performed using GraspIt!'s AutoGrasp function. Each Fribble was grasped 24 times, with the fribble rotated 8 times (by 45°) on each axis. To be sure that Fribbles fit inside GraspIt!'s hand, their sizes were reduced by 29%. Please see the <a href="http://www2.bcs.rochester.edu/sites/jacobslab/images/fb.png">figure</a> for sample images from the dataset and an illustration of the grasping procedure.</p> <p>The file contains three folders and a CSV file. The folders “Fa”, “Fb”, and “Fc” contain the files for each family of Fribble we examined—A, B, and C. These files each contain the following folders:</p> <ul> <li>“renders” contains the images of each Fribble in the species.</li> <li>“obj” contains the MTL and OBJ files for each Fribble in the species.</li> <li>“parts” contains the original Fribble parts from Mike Tarr's web pages which are composited into the individual Fribbles.</li> <li>“vrml” contains the VRML file for each Fribble in the species.</li> </ul> <p>The files are named based on the parts which were combined to make that Fribble. E.g., 4_a1b3c2d3 was made by combining 4_body.obj, 4_a1.obj, 4_b3.obj, 4_c2.obj, and 4_d3.obj.</p> <p>Finally, “haptics.csv” contains the haptic features for each of the Fribbles. It is a CSV file with 892 rows (including one header) and 255 columns. The first column is the Fribble's species and name, the next 254 columns are the joint angles from Graspit!: we performed a grasp for each of 24 different angles (rotating 45° around each axis), and each grasp has 16 DOF values associated with it, representing the position of the hand's joints.</p> <p>The rest of the columns contain the data for the contact points between the Fribble and the hand in Graspit. The first number for each grasp (there are 24 grasps for each Fribble) is the number of contact points for that grasp. For each contact point, the first six numbers relate to the contact's wrench, the next 3 numbers specify the contact's location in body coordinates, and the last number specifies the scalar constraint error for that contact.</p> <p>Sample code for rendering objects visually with VTK and haptically with GraspIt can be seen in the folder SampleCode. Please read GraspItUpdate.pdf file to see how to update GraspIt’s TCP server code for haptic rendering. Sample code folder also contains aomr.xml, the world file (i.e., setup of the human hand) used for haptic rendering in GraspIt. <br> <br> Please cite the following paper in relation to the See & Grasp 2 data set. <br> <br> Yildirim, I. & Jacobs, R. A. (2013). Transfer of object category knowledge across visual and haptic modalities: Experimental and computational studies. Cognition, 126, 135-148.</p> <p>Citation for GraspIt!<br> Miller, A., & Allen, P. K. (2004). Graspit!: A versatile simulator for robotic grasping. IEEE Robotics and Automation Magazine, 11, 110-122.</p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_characterization of sensing system for grippers_v0
<p><span>The dataset contain the data acquired from the multi-sensorized fingers developed in T5.1 and integrated into grippers used in IntelliMan UC3 and UC4. The data contain tactile data, proximity data and endoscopic camera data for the evaluation of sensor performance with respect to IntelliMan use cases requirements.</span></p>
Bimanual Grasping adheres to Weber's Law
<p>Datasets associated with the following publication:</p> <p><em>Hesse, C., Harrison, R. E., Giesel, M., & Schenk, T. (2021). Bimanual Grasping adheres to Weber’s Law. i-Perception (under revision)</em></p> <p>The folder contains four data files. </p> <p>DATA_MGA_N20.txt: contains the size of the maximum hand opening for grasping and the size of the aperture during manual estimation for each trial and every participant</p> <p>DATA_EndPos_N20.txt: contains the position of each hand (left and right) on the box at the moment the object was lifted in grasping trials.</p> <p>SummaryDATA_SD_N20.txt and SummaryDATA_IQR_N20.txt</p> <p>contain the variability of the maximum hand opening (indicating JNDs) in grasping and the estimation aperture in the perceptual task for every participant and condition (can also be calculated from the single trial data) once determined as standard deviations (SD) and once determined as the interquartile range (IQR).</p> <p>There is a separate description file (README.rtf) providing further relevant information for the data files uploaded.</p> <p>For further questions, please contact:<br> c.hesse[at]abdn.ac.uk</p>
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OpenNeuro
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