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2,639 results for “Robotic”
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots (video results)
<p>Video result of submitted paper "FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots" in ICARSC confrence</p>
Description of mobile robotics competitions
<p>This dataset contains relevant information about mobile robotics competitions that take place over the last few years, as well as the URLs to papers related to each competition. The dataset was obtained from a systematic mapping literature review.</p>
Reconstructing Robot Motion from Video
<p>Dataset for "Reconstructing Robot Motion from Video". The dataset includes images for Baxter and Spot experiments and ground truth joint angles for Baxter experiments.</p>
Single-site robotic cholecystectomy versus single-incision laparoscopic cholecystectomy: A systematic review and meta-analysis
<p>Single-site robotic cholecystectomy versus single-incision laparoscopic cholecystectomy: A systematic review and meta-analysis</p>
AlienGo Quadruped Robot Bags
<p>"AlienGo Quadruped Robot Bags" is a public dataset containing sensor data recorded in the AlienGo quadruped robot.<br> Overall, most of the bags include the following topics:</p> <ul> <li><strong>/livox/imu</strong> - Livox Mid-70 LiDAR data in the Livox customized format</li> <li><strong>/Aliengo/imu</strong> - inertial data regarding the robot's IMU</li> <li><strong>/tracking_camera/imu</strong> - inertial data coming from the T265 tracking camera</li> <li><strong>/tracking_camera/fisheye2/image_raw</strong> - T265 camera raw images (not included in all bags)</li> <li><strong>/tracking_camera/odom/sample</strong> - T265 camera odometry (not included in all bags)</li> </ul> <p>Despite that, there are two bags containing data from the Ouster OS1-64 LiDAR (ouster_*.bag). One indoors, and the other outdoors.</p>
Dataset of industrial robots
<p>Dataset of industrial robots, including ABB, COMAU, KUKA, NACHI, UFACTORY, and UR robots.</p>
Magnetically actuated gearbox for the wireless control of millimeter-scale robots
<p>The limited force or torque outputs of miniature magnetic actuators constrain the locomotion performances and functionalities of magnetic millimeter-scale robots. Here, we present a magnetically actuated gearbox with a maximum size of 3 millimeters for driving wireless millirobots. The gearbox is assembled using microgears that have reference diameters down to 270 micrometers and are made of aluminum-filled epoxy resins through casting. With a magnetic disk attached to the input shaft, the gearbox can be driven by a rotating external magnetic field, which is not more than 6.8 millitesla, to produce torque of up to 0.182 millinewton meters at 40 hertz. The corresponding torque and power densities are 12.15 micronewton meters per cubic millimeter and 8.93 microwatt per cubic millimeter, respectively. The transmission efficiency of the gearbox in the air is between 25.1 and 29.2% at actuation frequencies ranging from 1 to 40 hertz, and it lowers when the gearbox is actuated in viscous liquids. This miniature gearbox can be accessed wirelessly and integrated with various functional modules to repeatedly generate large actuation forces, strains, and speeds; store energy in elastic components; and lock up mechanical linkages. These characteristics enable us to achieve a peristaltic robot that can crawl on a flat substrate or inside a tube, a jumping robot with a tunable jumping height, a clamping robot that can sample solid objects by grasping, a needle-puncture robot that can take samples from the inside of the target, and a syringe robot that can collect or release liquids.</p>
Quadruped Robot IRON DOG mini
<p>12 DoF Quadruped Robot IRON DOG mini</p>
Video Demonstrations - MQTT Enabled Simulation Interface for Motion Execution of Industrial Robots
<p>Video demonstration of two applications showing the use of the MQTT interface in VEROSIM. In the first one, a robotic simulation is recorded using the MQTT interface and later deployed on a real robot. In the second one, an external client sets target poses of the robot and obtains a simulation of the motion. </p>
VIMA: General Robot Manipulation with Multimodal Prompts
<p>VIMA dataset for learning general robot manipulation with multimodal prompts.</p>
Change Detection in Dynamic Environments by Means of a Robotic Swarm - Digital Appendix
<p>Digital Appendix for my thesis Change Detection in Dynamic Environments by Means of a Robotic Swarm.</p> <p>In the zip-file you find a README that guides you through the structure and where to find the plots for the respective experiments.</p> <p><strong>Change Detection of Dynamic Environments by Means of a Robotic Swarm - Digital Appendix</strong></p> <p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (BOCPD, PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folders of BOCPD and PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Supplemental material: Comparing approaches for evolving high-level robot control based on behaviour repertoires
<p>Supplemental material for the paper:</p> <p>Comparing approaches for evolving high-level robot control based on behaviour repertoires<br> Jorge Gomes, Anders Lyhne Christensen</p> <p>Videos of the highest-performing solutions evolved with each method. In all videos, the schematic of the robot indicates the current wheel angles and robot speed. In the repertoire-based methods, the visualization on the bottom left indicates which primitives are being executed.</p> <p><strong>vid_evorbc_2_4_f1.12.mp4 </strong><br> Evolved by NEAT-EvoRBC. The blue cross indicates the behaviour selection vector.</p> <p><strong>vid_gp_2_2_f1.12_best.mp4</strong><br> Evolved by GP-DT. The primitives highlighted in green are the ones that exist in the decision tree.</p> <p><strong>vid_gp_8_4_f0.92_paper.mp4 </strong><br> Evolved by GP-DT. Smallest tree with fitness above 0.9. Shown in the paper.</p> <p><strong>vid_neatsub_9_5_f0.89.mp4 </strong><br> Evolved by NEAT-Subset. Only the primitives that are in the subset are shown. The numbers near each primitive indicate the activation level of the corresponding output (or maximum activation level if there are multiple outputs corresponding to the same primitive).</p> <p><strong>vid_neattr_24_f0.28.mp4 </strong><br> Evolved by NEAT-TR.</p> <p>The videos have been verified to reproduce correctly with the <strong>VLC player</strong>. But any other modern player should handle them.</p>
Data for "What it Takes to Get There: Spatial Cognition and Autonomous Indoor Robot Navigation"
Open the record for dataset details and reuse information.
Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)
<p>Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR".</p> <p>Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated:</p> <ul> <li>a dataset that contains YOLO annotations for these images.(this dataset)</li> <li>a second dataset contains images of Sheep, (10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. ( 10.5281/zenodo.11313966)</li> </ul> <p> </p>
Fostering Inclusivity and Equality in Early Childhood Education via Computational Thinking and Robotics: A Systematic Review
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2023.</p>
Validation of LEXO® End-Effector Robot-assisted Training in Patients with Gait Deficits after Central Nervous System Diseases. A Descriptive Cross-sectional Study.
Open the record for dataset details and reuse information.
Supplementary footage for ctenophore-inspired soft robotic platform
<p>These three folders contain supplementary footage for “Encoding spatiotemporal asymmetry in artificial cilia with a ctenophore-inspired soft-robotic platform”.</p> <p>“Phase-averaged” contains gifs of phase averaged horizontal component of velocity (u), velocity magnitude, and vorticity. Note the folder naming convention denotes the shape of the propulsor, followed by the number of magnets on the timing belt (low or high phase lag), followed by the beat frequency.</p> <p>“PIV” contains videos of the flow fields computed with Particle Image Velocimetry. The horizontal component of velocity and vorticity are included for each propulsor shape, phase lag, and beat frequency listed above.</p> <p>“Raw_footage” contains videos of the raw footage used for kinematics and PIV.</p>
Video Data (4K): Purely vision-based collective movement of robots
<p><strong>This dataset holds the four main experimental videos in 4K resolution of the paper titled "<em>Purely vision-based collective movement of robots</em>" by Mezey et al. (2024).</strong></p> <p><strong>Abstract:</strong></p> <p>Collective movement inspired by animal groups promises inherited benefits for robot swarms, such as enhanced sensing and efficiency. However, while animals move in groups using only their local senses, robots often obey central control or use direct communication, introducing systemic weaknesses to the swarm. In the hope of addressing such vulnerabilities, developing bio-inspired decentralized swarms has been a major focus in recent decades. Yet, creating robots that move efficiently together using only local sensory information remains an extraordinary challenge. In this work, we present a decentralized, purely vision-based swarm of terrestrial robots. Within this novel framework robots achieve collisionless, polarized motion exclusively through minimal visual interactions, computing everything on board based on their individual camera streams, making central processing or direct communication obsolete. With agent-based simulations, we further show that using this model, even with a strictly limited field of view and within confined spaces, ordered group motion can emerge, while also highlighting key limitations. Our results offer a multitude of practical applications from hybrid societies coordinating collective movement without any common communication protocol, to advanced, decentralized vision-based robot swarms capable of diverse tasks in ever-changing environments.</p> <p><strong>The video files are as follows:</strong></p> <ol> <li>Video 1: Video of a ca. 90 minute experiment with robots flocking aligned with the arena wall</li> <li>Video 2: Demonstrating fission and later fusion of a single robot</li> <li>Video 3: Demonstrating fisiion/fusion behavior of 2 robots</li> <li>Video 4: Showcasing decentralized design by adding/removing robots on the fly.</li> </ol> <p>The videos are also avialable on the TIB-AV portal in 1080p resolution where they can be cited individually under the DOIs:</p> <ol> <li>Video 1: https://doi.org/10.5446/68205</li> <li>Video 2: https://doi.org/10.5446/68204</li> <li>Video 3: https://doi.org/10.5446/68203</li> <li>Video 4: https://doi.org/10.5446/68201</li> </ol> <p><strong>Funding:</strong></p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135.</p>
ENHANCED ROBOTICS
<p>Data depository for publication.</p>
ScienceDex guides
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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.