Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
99
datasets available to search
ShareScore release 0.9.0
Dataset results
99 results for “robotic arm”
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</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>
Piezoresistive sensor fiber composites based on silicone elastomers for the monitoring of the position of a robot arm
<p>Combining conductive fillers like carbon black with elastomers allows the development of soft elastomer strain sensors that can reach very large elongations, an important requirement for many robotic applications. However, when the conductive filler is introduced in the polymer, significant stiffening occurs, affecting the mechanical properties, e.g. Young’s Modulus, of the soft structure. In this attempt, single piezoresistive fiber composites were successfully fabricated, without drastically increasing the stiffness. Two silicone elastomers that are widely used in robotic applications were examined as matrix materials. Furthermore, modeling the stresses exerted on the fiber inside the composite was successfully used to predict the detachment of fiber inside the matrix, observed by visual inspection. For the PDMS based composite, pre-straining improved sensor properties, which could be confirmed for the monitoring of the movement of the crane robot. The results showed that the pre-strained piezoresistive sensor fiber-matrix composites positions of the robot crane can be monitored even at low strains.</p>
Arm gesture dataset based on IMU data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of ten different/independent hand gestures are provided (seven static gestures and three dynamic gestures). The data were generated in a IMU system with five sensors worn in the right forearm, right arm, chest, left arm and left forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetStaticGestures.mat" was used to train and test a classifier whose purpose is the recognition of static gestures. On the other hand, the file "datasetDynamicGestures.mat" was used to train and test a classifier whose purpose is the recognition of dynamic gestures. The latter file contains an extra class (gesture) which represents non-gestures.</p>
Modular Components for synthesizing Robotic Arms with CLS-CAD
<p>A set of modular components as Autodesk Fusion 360 files suitable for synthesizing robotic arms with CLS-CAD (https://github.com/tudo-seal/CLS-CAD). The files contain the type information required for synthesis as well as the necessary taxonomy. </p><p>To use these files or reproduce results, please follow the instructions available at https://github.com/tudo-seal/CLS-CAD in the "Getting Started" Section.</p>
Robot Arm
<p>This is a pre-processed version of the dataset saved in numpy format.</p> <p>The data are 3-dimensional arrays of shape [n_samples, time_steps, n_variables]. The data can be loaded as follows:</p> <pre><code>loaded_data = np.load("Robot.npz") Xtr = loaded_data['Xtr'] # Training data of shape (100, 15, 6) Ytr = loaded_data['Ytr'] # Training labels of shape (100, 1) Xte = loaded_data['Xte'] # Test data of shape (64, 15, 6) Yte = loaded_data['Yte'] # Test labels of shape (64, 1)</code></pre>
Motor-Imagery EEG Dataset During Robot-Arm Control
<p><strong>Experiment Description:</strong></p> <p>This experiment involved <strong>12 healthy subjects</strong> with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p>The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through <strong>three sessions</strong> lasting maximum two hours, during three consecutive days and each day at approximately at the same hour.</p> <p>During each session, participants underwent <strong>three different conditions</strong>. The first condition was always the ”<em>resting-state</em>”: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of<strong> two phases</strong> each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task.</p> <p>Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds.</p> <p>Overall, this study resulted into <strong>180 EEG </strong>datasets.</p> <p> </p> <p><strong>Data Description:</strong></p> <table> <tbody> <tr> <td><strong>Data Format</strong></td> <td>General Data Format (GDF)</td> </tr> <tr> <td><strong>Sampling Rate</strong></td> <td>250 Hz</td> </tr> <tr> <td><strong>Channels</strong></td> <td>32 EEG + 3 ACC.</td> </tr> <tr> <td><strong>EEG system</strong></td> <td>LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany)</td> </tr> </tbody> </table> <p> </p> <p><strong>Events:</strong></p> <table> <caption> </caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>32775</td> <td>Baseline Start</td> </tr> <tr> <td>32776</td> <td>Baseline Stop</td> </tr> <tr> <td>768</td> <td>Start of Trial, Trigger at t=0s</td> </tr> <tr> <td>786</td> <td>Cross on screen (BCI experiment)</td> </tr> <tr> <td>33282</td> <td>Beep</td> </tr> <tr> <td>769</td> <td>class1, Left hand - cue onset</td> </tr> <tr> <td>770</td> <td>class2, Right hand - cue onset</td> </tr> <tr> <td>781</td> <td>Feedback (continuous) - onset</td> </tr> <tr> <td>800</td> <td>End Of Trial</td> </tr> <tr> <td>1010</td> <td>End Of Session</td> </tr> <tr> <td>33281</td> <td>Train</td> </tr> <tr> <td>32770</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory Tree:</strong></p> <p>ROOT<br> | chanlocs.locs<br> |<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---CONDITION #<br> | | | \---RESTING_STATE<br> | | | +---1st_PERSON<br> | | | | TRAINING<br> | | | | ONLINE<br> | | | +---3rd_PERSON<br> | | | | TRAINING<br> | | | | ONLINE</p>
ARMS robot
<p>The Agricultural Robotic Manipulator for Spectrometry (ARMS) was designed during the development of the thesis 'Design of a low-cost SCARA manipulator for agricultural monitoring robots' by Maria Rehbein Pinheiro. The goal was to develop and investigate a mobile robot that incorporates a SCARA manipulator for spectral data collection in agriculture. This manipulator should facilitate a mobile platform's ability to monitor tree crops and gather data from nearby sources, such as leaves and fruits. More specifically, the system needs to analyze its surroundings, identify targets, approach them if necessary using the mobile platform, and manoeuvre the manipulator's end effector to make contact with the fruit or leaf to collect the required data for assessing their quality. The collection and data analysis are outside of the system's spectrum, the goal is the automatically guide the sensor in the end-tool.<br>This video presents the actuation of the ARMS robot in different situations, such as single target approach, colour differentiation, and multiple target approach.</p>
Digital Twin of a Multi-Arm Robot Platform based on Isaac Sim for Synthetic Data Generation
<p>This data set is required by the following repository<br> https://github.com/AISciencePlatform/icra2023_synthetic_data_pretraining_for_robotics</p>
Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
<p>Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or pre-recorded expert demonstrations. Here, we propose a minimally-guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in pre-modeled environments. The key insight is that multi-modal problems of this nature can be modeled within the context of integrated Task and Motion Planning (TAMP), resulting in a tractable bilevel optimization formulation. An effective bilevel search strategy is achieved owing to the fusion of domain-specific rules with the well-established strengths of different planning techniques: trajectory optimization and informed graph search, coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and non-prehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors are also deployed on the real system using a two-layer whole-body tracking controller.</p>
Transcutaneous Vagus Nerve Stimulation (tVNS) and Robotic Training to Improve Arm Function After Stroke
ClinicalTrials.gov study NCT03592745. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Evaluation of Robotic Arm Rehabilitation in Stroke Patients
ClinicalTrials.gov study NCT00333983. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Combined Transcranial Direct Current Stimulation and Motor Imagery-based Robotic Arm Training for Stroke Rehabilitation
ClinicalTrials.gov study NCT01897025. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A Single-arm Phase II Study of Post-Transoral Robotic Surgery (TORS) Alone to the Primary Tumor Site and Selective Neck Dissection (SND) Followed by Adjuvant Radiation Therapy (+/- Chemotherapy) to th
ClinicalTrials.gov study NCT02159703. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Prospective, Single-center, Single-arm, Pivotal Trial of the Medtronic Hugo™ Robotic Assisted Surgery (RAS) System
ClinicalTrials.gov study NCT05715827. IPD Sharing: NO. Countries: 1. Publications: 1.
Two Year Study With Robotic-Arm Assisted Hip Surgery.
ClinicalTrials.gov study NCT03891199. IPD Sharing: YES. Countries: 1. Publications: 19.
Data from: Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks
Open the record for dataset details and reuse information.
Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
Open the record for dataset details and reuse information.
Dataset to "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture"
<p>This dataset is related to the paper "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture".</p> <p>Contents:</p> <p>Recordings comparing a human performance and two robot performances (named case A and case B).</p> <ul> <li>Synchronised motion-capture data (time, bow markers, nut marker and bridge marker; Fs = 240 fps - .csv files)</li> <li>Synchronised audio recordings (sound in front of the instrument; Fs = 44100 Hz - .wav files)</li> <li>Music score</li> <li>Read-me file with further details</li> </ul>
Bimanual Robot Arm Cloth Manipulation Dataset
<p>This dataset contains 945 trials of a bimanual robot arm setup, where one arm holds a piece of cloth (e.g., a towel) by one corner while the other arm, equipped with a wrist-mounted camera, captures images from approximately 180 different angles by making a half-circle motion around the cloth. Each trial includes images and the corresponding TCP (Tool Center Point) poses of the second arm. Additionally, the dataset provides annotated positions of the two corners adjacent to the held corner, which are critical for unfolding the cloth by grasping these points. This dataset is valuable for research in robotic manipulation, computer vision, and cloth handling tasks.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.