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135 results for “Grasping”
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
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Data to "Predicting precision grip grasp locations on three-dimensional objects"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Klein, L. K. ^, Maiello, G. ^, Paulun, V. C., & Fleming, R. W. (in press). <br> Predicting precision grip grasp locations on three-dimensional objects. PLOS Computational Biology<br> ^co-first authors </p> <p>A preprint version of the manuscript is currently available at: https://doi.org/10.1101/476176</p>
Dataset used for evaluation of GRASP-AOD algorithm
<p><strong>Dataset used for evaluation of GRASP-AOD algorithm</strong></p> <p>30 AERONET sites where processed using GRASP v1.0.0 and following the methodology described in Torres et. al 2017 and Torres et Fuertes 2020. More sites and data can be found at <a href="http://www.grasp-open.com">www.grasp-open.com</a> .</p> <p>31 files can be found. 30 Files described the 30 AERONET sites used for GRASP-AOD validation while the file aureole_Granada.csv is used in the section 4.2 for GRASP-AUR test.</p> <p>Description of the columns that can be found in the datafiles:</p> <ul> <li>date</li> <li>FineModeMedianRadius</li> <li>FineModeGeometricStandardDeviation</li> <li>FineModeVolumeConcentration</li> <li>CoarseModeMedianRadius</li> <li>CoarseModeGeometricStandardDeviation</li> <li>CoarseModeVolumeConcentration</li> <li>VolumeConcentration</li> <li>Effective radius</li> <li>abs_error</li> <li>rel_error</li> <li>aod500_fine</li> <li>aod500_coarse</li> <li>aod380_retrieved</li> <li>aod440_retrieved</li> <li>aod500_retrieved</li> <li>aod870_retrieved</li> <li>aod1020_retrieved</li> <li>input380nm</li> <li>input440nm</li> <li>input500nm</li> <li>input870nm</li> <li>input1020nm</li> <li>exist_380nm</li> <li>exist_440nm</li> <li>exist_500nm</li> <li>exist_870nm</li> <li>exist_1020nm</li> <li>number_of_wavelengths</li> <li>min_wavelength</li> <li>max_wavelength</li> <li>climatology_method</li> <li>wavelengths_used</li> </ul> <p><strong><em>Please follow the data policy of each data source:</em></strong></p> <p>GRASP-AOD: GRASP-OPEN (<a href="https://www.grasp-open.com/products/">https://www.grasp-open.com/products/</a>)</p> <p>AERONET: <a href="http://www.aeronet.gsfc.nasa.gov/">http://www.aeronet.gsfc.nasa.gov</a></p> <p>Details can be found in manuscript:</p> <p>Torres B. et Fuertes D., Characterisation of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data, sent to review on 2020</p>
Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Real-world grasp data of a dual-arm Yumi robot with a parallel gripper and suction cup end-effectors
<p>The attached txt file contains indexes to a cleaner subset of the data issued in the first version.</p> <p>Note: <br>Version 1 contains samples with failure cases due to environment constraints, which work well for the platform used in GraspAgent 1.0 (https://doi.org/10.1109/LRA.2024.3502066). However, this can degrade the performance if used on another platform with different constraints. To solve this, version 2 reports a subset of the raw data, excluding the failure modes due to environmental causes. </p>
Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022
<p>Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as <em>Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022</em> have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.</p> <p>Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.</p> <p>The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.</p> <p>Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.</p> <p>More information on our methods, data sources, and data processing and analysis codes are found on our <a href="https://github.com/C-GRASP">github page </a></p> <p>The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files</p> <ol> <li>dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline</li> <li>Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon</li> <li>Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data</li> <li>Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012</li> </ol> <p>The files each have the following fields (no data is blank):</p> <p>'ID': row ID integer</p> <p>'Sample_ID': identifier to raw data source</p> <p>'Sample_Type_Code': code of sample id</p> <p>'Project': raw datasource project identifier</p> <p>'dataset': raw dataset major identifier</p> <p>'Date': date, where specified, and to whatever precision that is specified</p> <p>'Location_Type': where specified, code indicating type of location information</p> <p>'latitude': latitude in decimal degrees</p> <p>'longitude': longitude in decimal degrees</p> <p>'Contact': where specified, raw data originator</p> <p>'num_orig_dists': number of unique grain size distributions</p> <p>'Measured_Distributions': number iof measured grain size distributions</p> <p>'Grainsize': grain size is sometimes reported without specification</p> <p>'Mean', mean grain size in mm</p> <p>'Median', median grain size in mm</p> <p>'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])</p> <p>'Kurtosis', kurtosis value (non-dim)</p> <p>'Kurtosis_Class', kurtosis category</p> <p>'Skewness', skewness value (non-dim)</p> <p>'Skewness_Class', skewness category</p> <p>'Std', standard deviation of grain sizes </p> <p>'Sorting', sorting category</p> <p>'d5', grain size distribution 5th percentile</p> <p>'d10', grain size distribution 10th percentile</p> <p>'d16', grain size distribution 16th percentile</p> <p>'d25', grain size distribution 25th percentile</p> <p>'d30', grain size distribution 30th percentile</p> <p>'d50', grain size distribution 50th percentile</p> <p>'d65', grain size distribution 65th percentile</p> <p>'d75', grain size distribution 75th percentile</p> <p>'d84',grain size distribution 84th percentile</p> <p>'d90', grain size distribution 90th percentile</p> <p>'d95', grain size distribution 95th percentile</p> <p>'Notes': notes - these can be informative and substantial, do not disregard</p> <p> </p> <p>Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format</p>
Present day human hand grasping the same artifact by hand and hafted
<p><em>Examples of a present day human hand demonstrating a precision grip (top left) when grasping an artifact by hand and a power "squeeze" grip (top right) when grasping a hafted artifact (both palmar view). In turquoise (first metacarpal) and purple (trapezium) are the present day human and Neanderthal bones forming the trapeziometacarpal complex at the base of the thumb and responsible for its movements. </em></p>
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
<p>This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on <a href="https://github.com/AuCoRoboticsMU/vgq-cnn">github</a>. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip.</p> <p> </p> <p>To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published in MDPI Electronics 2021
<p>The datasets contain data for the normalization of tactile voltages, the training dataset for the diameter classifier, and data recorded during the experiment related to the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Tactile sensor data interpretation for estimation of wire features,” Electronics, vol. 10, no. 12, art. 1458, June 2021. (DOI: 10.3390/electronics10121458)</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper for the conference ICPS2022
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Wire Grasping by Using Proximity and Tactile Data,” 5th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2022. (DOI: 10.1109/ICPS51978.2022.9816936)</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published in IEEE Access 2021
<p>The datasets contain images for the training and testing of algorithms presented in the publication:</p> <p>Cirillo, P., Laudante, G., Pirozzi, S. “Vision-Based Robotic Solution for Wire Insertion with an Assigned Label Orientation” (2021) IEEE Access, 9, art. no. 9490630, pp. 102278-102289. DOI: 10.1109/ACCESS.2021.3098472</p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-2. Cable grasping. Data related to a paper published on RA-L (2022)
<p>The dataset contains data related to the following publication:</p> <p>Costanzo, M., De Maria, G., Natale, C., Russo, A., “Stability and Convergence Analysis of 3D Feature-Based Visual Servoing”, (2022) IEEE Robotics and Automation Letters, pp. 1-8. (DOI: 10.1109/LRA.2022.3211154)</p>
Fig. 7 in Chaetognath grasping spines from the Devonian of Poland: their structure and geochemistry
Fig. 7. Back scattered electron images of thin section made of Devonian Phakeloides polonicus gen. et sp. nov. spines from the upper Famennian, Ostrówka Quarry, Holy Cross Mountains, Poland. A. Longitudinal section of the spine showing the apatite phases of similar density of the middle and inner layers, ZPAL Cg. 2/Ost-Ch.48. B. Longitudinal section of the spine showing the apatite phases of similar density of the middle and inner layers as well as a thin cortex of slightly higher density, which may represent fragmentarily preserved outer layer, ZPAL Cg. 2/Ost-Ch.49.
Fig. 5 in Chaetognath grasping spines from the Devonian of Poland: their structure and geochemistry
Fig. 5. Transmitted light (A1, B1) and hot cathodoluminescence images (A2, B2) of Devonian Phakeloides polonicus gen. et sp. nov. grasping spine A, ZPAL Cg. 2/Ost-Ch.41) and Polygnathus sp. conodont element (B, ZPAL Cg. 2/Ost-C.45) from the upper Famennian, Ostrówka Quarry, Holy Cross Mountains, Poland. A2, weak to moderate red cathodoluminescence of the grasping spine and bright luminescence of some parts of its outermost rim area. B2, very weak yellow-red luminescence of conodont albid tissue and non-luminescent hyaline tissue.
Fig. 1 in Chaetognath grasping spines from the Devonian of Poland: their structure and geochemistry
Fig. 1. Geographical location (A) and simplified geologic map (B) of the Holy Cross Mountains with location of the studied exposure. C. Cross-section through the ledge of the Ostrówka Quarry. 1, peritidal carbonates (Kowala Formation); 2, condensed crinoidal-cephalopod limestone beds; 3, clay with limestone intercalations; 4, black radiolarian shale (Zaręby Beds); 5, carbonate gravity flows (after Szulczewski et al. 1996).
Fig. 4 in Chaetognath grasping spines from the Devonian of Poland: their structure and geochemistry
Fig. 4. Scanning electron microscope images of Devonian chaetognath grasping spines Phakeloides polonicus gen. et sp. nov. (A–D) and conodont elements (E) from the upper Famennian, Ostrówka Quarry, Holy Cross Mountains, Poland. A. ZPAL Cg. 2/Ost-Ch.31, cross-section throughout the spine showing massive mineral structure of the middle layer characterized by the conchoidal fracture, a reticular ornamentation of the surface of the middle layer is partially visible. B. ZPAL Cg. 2/Ost-Ch.32, longitudinal section throughout the spine showing a massive mineral structure characterized by uneven fracture, which is superimposed on minor breaks forming the second-order cleavage plane. A thin, cylindrical internal layer composed of finegrained calcium phosphate crystals is visible in the innermost portion of the spine wall, around the internal cavity. C. ZPAL Cg. 2/Ost-Ch.33, a surface of the middle layer of the spine showing oblique to the spine axis, primary fibrils, visible at its slightly, broken part, and less noticeable surface striations of the reticular ornamentation. D. ZPAL Cg. 2/Ost-Ch.34, D1, cross-section throughout the spine showing porous internal zones of the middle layer, which are divided by solid cylindrical zones, a thin, cylindrical internal layer is visible in the innermost portion of the spine wall; D2, three layers in the wall of the basal portion of the spine: a thin, most internal, layer (a), the middle layer (b), and the outer layer (c). E. ZPAL Cg. 2/Ost-C.38, cross-section throughout Polynodosus sp. conodont element showing innermost pillar-like structure of densely packed, weakly lamellar, albid (white matter) tissue, and outer hyaline tissue composed of oblique apatite lammelae. The outermost portion of the conodont element consists of paralamellar variety of the hyaline tissue of more chaotic structure.
Human Grasp Dataset for Human Robot Handovers
<p>The dataset consists of 278.400 RGB images of size (299, 299).</p> <p>The images are sorted into the folders:</p> <ul> <li>Angles <ul> <li>30°, 45°, and 60°</li> </ul> </li> <li>Lights <ul> <li>From_Behind, From_Front, and Full_Lighting</li> </ul> </li> <li>Objects <ul> <li>Duplo_Block, Highlighter, Plastic_Pear, Table_Tennis_Racket, and Wood_Block</li> </ul> </li> <li>Persons <ul> <li>Person_1 to Person_10</li> </ul> </li> <li>Other <ul> <li>Default_Configuration, Clutter, and Other_Grasps_And_Interactions</li> </ul> </li> </ul> <p>Each folder contains 11.600 images that have a label in their file name. l=1 for grasp and l=0 for not grasp. </p>
Fig. 4 in Chaetognath grasping spines from the Upper Mississippian of Arkansas (USA)
Fig. 4. Eoserratosagitta serrata gen. et sp. nov., Cove Creek (Arkansas, USA), Middle Chesterian (Namurian A equivalent). A. Three relatively complete paratypes (OUZC 4002/B, C, D from top to bottom, respectively) with nearly identical basic morphology including bands (b) and bulletlike denticles (d), scale bar 1.0 mm. B. Enlargement of the lower right segment of paratype OUZC 4002/B showing the phosphate replaced, fibrous ultrastructure of the spine wall, scale bar 0.1 mm. All SEM micrographs.
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