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2,639 results for “robotic”
Dataset for: Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies
<p>Dataset containing electronic, mechical, firmware, and software design files associated with the article: </p> <p><br>"Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies"<br>By R. Barmak, D. N. Hofstadler, M. Stefanec, L. Piotet, R. Cherfan, T. Schmickl, F. Mondada, and R. Mills. EPFL, Switzerland and Univeristy of Graz, Austria.<br>IEEE Access, 2024, Vol 12, pp 50849-50871.</p> <p>doi: 10.1109/ACCESS.2024.3385658</p> <p><a href="https://doi.org/10.1109/ACCESS.2024.3385658">https://doi.org/10.1109/ACCESS.2024.3385658</a></p> <p> </p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1_hw_pcb_schematics_rev2.pdf</td> <td>Electrical schematic of the robotic frame</td> </tr> <tr> <td>2_hw_pcb_stackup_rev2.pdf </td> <td>Technical specifications for the robotic frame PCB manufacturing</td> </tr> <tr> <td>3_hw_pcb_bom_rev2.pdf </td> <td>Electronics Bill of Materials (BoM)</td> </tr> <tr> <td>4_hw_pcb_gerber_rev2.zip </td> <td>Robotic frame PCB manufacturing files (gerbers)</td> </tr> <tr> <td>5_hw_pcb_altium_rev2.zip</td> <td>Altium Designer project files</td> </tr> <tr> <td>6_hw_mechanical_rev2.zip</td> <td>DXF and STEP files of the mechanical structure of the robotic frame</td> </tr> <tr> <td>7_sw_firmware_rev2.zip</td> <td>Firmware source code and compiled binaries for STM32 microcontroller</td> </tr> <tr> <td>8_sw_handlers-1.0.1.zip</td> <td>Software for high-level interface to robot from a host device </td> </tr> </tbody> </table>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
Data for "Adaptive and Resilient Soft Tensegrity Robots" (Rieffel & Mouret, 2018)
<p>Data (experimental results) for the paper "Adaptive and Resilient Soft Tensegrity Robots", to appear in Soft Robotics (2018).</p> <ul> <li>Source code: <a href="https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity">https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity </a></li> <li>Pre-print: <a href="https://arxiv.org/abs/1702.03258">https://arxiv.org/abs/1702.03258</a></li> </ul>
Hand gesture dataset based on sEMG data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of five different/independent hand gestures are provided. The data were generated in a sEMG system with two bracelets (eight sEMG sensors and six sEMG sensors) worn in the right forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetForSegmentation.mat" was used to train a classifier whose purpose is the execution of Segmentation process. On the other hand, the file "datasetForRecognition.mat" was used to train a classifier whose purpose is the execution of gesture Recognition process.</p> <p> </p>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</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>
Children's Reliance on the Non-Verbal Cues of a Robot Versus a Human
<p>This study assessed whether four- to six-year-old children (i) differed in their weighing of non-verbal cues (pointing, eye gaze) and verbal cues provided by a robot versus a human; (ii) weighed non-verbal cues differently depending on whether these contrasted with a novel or familiar label; and (iii) relied differently on a robot’s nonverbal cues depending on the degree to which they attributed human-like properties to the robot. The results showed that children generally followed pointing over labeling, in line with earlier research. Children did not rely more strongly on the non-verbal cues of a robot versus those of a human. Regarding pointing, children who perceived the robot as human-like relied on pointing more strongly when it contrasted with a novel label versus a familiar label, but children who perceived the robot as less human-like did not show this difference. Regarding eye gaze, children relied more strongly on the gaze cue when it contrasted with a novel versus a familiar label, and no effect of anthropomorphism was found. Taken together, these results show no difference in the degree to which children rely on non-verbal cues of a robot versus those of a human and provide preliminary evidence that differences in anthropomorphism may interact with children’s reliance on a robot’s non-verbal behaviors.</p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
Web robot detection - Server logs
<p>This dataset contains server logs from the search engine of the library and information center of the Aristotle University of Thessaloniki in Greece (<a href="http://search.lib.auth.gr/">http://search.lib.auth.gr/</a>). The search engine enables users to check the availability of books and other written works, and search for digitized material and scientific publications. The server logs obtained span an entire month, from March 1st to March 31 2018 and consist of 4,091,155 requests with an average of 131,973 requests per day and a standard deviation of 36,996.7 requests. In total, there are requests from 27,061 unique IP addresses and 3,441 unique user-agent strings. The server logs are in JSON format and they are anonymized by masking the last 6 digits of the IP address and by hashing the last part of the URLs requested (after last /). The dataset also contains the processed form of the server logs as a labelled dataset of log entries grouped into sessions along with their extracted features (simple semantic features). We make this dataset publicly available, the first one in this domain, in order to provide a common ground for testing web robot detection methods, as well as other methods that analyze server logs.<br> <br> </p>
The DARRL dataset: Demonstrations for Action Recognition and Robot Learning
<p>The DARRL dataset (Demonstrations for Action Recognition and Robot Learning) is a collection of 760 RGB-D videos of humans performing various manipulation tasks. It is provided with object and action annotations (in the COCO format) for 30 of those videos; segmentation masks are also provided.</p> <p>It can also be used as a basis for learning from demonstrations for a robotic arm, for instance.</p> <p> </p> <p>This work is supported by Région Pays de la Loire.</p>
Robust Acoustic Reflector Localization for Robots
<p>In this repository, we share our MATLAB code and dataset used to perform the experiments listed within our paper "<strong>Robust Acoustic Reflector Localization for Robots.</strong>"</p>
VHR images were produced in the tunnel tubes of EOAE by autonomous robotic systems (2022)
<p>In the context of the EU-funded project PILOTING (No. 871542), several validation scenarios were scheduled in the three pre-determined pilot sites, i.e. refinery, viaduct, and tunnel, in order to evaluate the good operation of 9 different robotic systems and a versatile data platform. Under this frame, the current dataset was generated during the pilot demonstrations in Metsovo tunnels on 30/9/2022-08/10/2022. The aforementioned were collected by two of the robotic systems, e.g. the CART and the TT-DRONE, covering the execution of the two inspections; a) the general inspection, where the robot covers all the tubes, and b) the local inspection, which captured images in dedicated positions. <br> This particular dataset is comprised of Very High Resolution (VHR) photos, gathered in the tunnel facilities of EGNATIA ODOS AE (EOAE). During the image capturing, no artificial flashlight was used. The total number of photos acquired from the general inspection is 133 and from the local inspection 8. The data format received by the two robotic systems is in JPEG with the CART vehicle producing images with the approximate dimensions of 9504x6336 pixels, and the TT-DRONE with 6000x4000 pixels. </p>
Pellet-based fused deposition modeling for the development of soft compliant robotic grippers with integrated
<p>Fused deposition modeling (FDM) has some advantages compared to other additive manufacturing techniques, such as the in situ integration of functional components, like sensors, and recyclability of parts. However, conventional filament-based FDM techniques are limited to thermoplastic elastomers with a Shore hardness above 70 A, thus it has marginal compatibility with soft robotic structures. Due to recently emerging pellet-based FDM printer technology, the fabrication of soft grippers with low Shore hardness has become possible. In this study, styrene based thermoplastic elastomers (TPS) were used to print elastic strips and soft gripper structures down to a Shore hardness of 25 A with an integrated strain sensing element (piezoresistive sensor). Printing on a soft rather than rigid substrate affects the integration of the printed thread on the substrate, because of the softness and relaxation, during the printing softness. It was seen that integrating the sensing element on a substrate with higher Shore hardness decreased the elongation at the point of fracture and the sensitivity of the sensing element. A soft compliant gripper structure with an integrated sensing layer was printed with the TPS-based elastomers successfully, and even due to the complex deformation of the compliant gripper structure, several positions could be detected successfully. Opened and closed position of the gripper, as well as, size recognition of spools of different sizes could be monitored by the piezoresistive printed sensor layer. The most sensitive sensing performance was obtained with the TPS of the lower Shore hardness (25 A), as the value of relative change in resistance was 1, followed by the gripper of Shore hardness 65 A and a relative change in resistance of 0.51. With this study, we demonstrated that pellet-based FDM printers can be used, to print potential soft robotic structures with in-situ integrated sensor structures.</p>
The Robot Tracking Benchmark (RTB)
<p>The Robot Tracking Benchmark (RTB) is a synthetic dataset that facilitates the quantitative evaluation of 3D tracking algorithms for multi-body objects. It was created using the procedural rendering pipeline BlenderProc. The dataset contains photo-realistic sequences with HDRi lighting and physically-based materials. Perfect ground truth annotations for camera and robot trajectories are provided in the BOP format. Many physical effects, such as motion blur, rolling shutter, and camera shaking, are accurately modeled to reflect real-world conditions. For each frame, four depth qualities exist to simulate sensors with different characteristics. While the first quality provides perfect ground truth, the second considers measurements with the distance-dependent noise characteristics of the Azure Kinect time-of-flight sensor. Finally, for the third and fourth quality, two stereo RGB images with and without a pattern from a simulated dot projector were rendered. Depth images were then reconstructed using Semi-Global Matching (SGM).</p> <p>The benchmark features six robotic systems with different kinematics, ranging from simple open-chain and tree topologies to structures with complex closed kinematics. For each robotic system, three difficulty levels are provided: easy, medium, and hard. In all sequences, the kinematic system is in motion. While for easy sequences the camera is mostly static with respect to the robot, medium and hard sequences feature faster and shakier motions for both the robot and camera. Consequently, motion blur increases, which also reduces the quality of stereo matching. Finally, for each object, difficulty level, and depth image quality, 10 sequences with 150 frames are rendered. In total, this results in 108.000 frames that feature different kinematic structures, motion patterns, depth measurements, scenes, and lighting conditions. In summary, the Robot Tracking Benchmark allows to extensively measure, compare, and ablate the performance of multi-body tracking algorithms, which is essential for further progress in the field.</p>
Will Biomimetic Robots Be Able to Change a Hivemind to Guide Honeybees' Ecosystem Services? (dataset)
<p>Simulation data for different simulation runs from the publication "Will Biomimetic Robots Be Able to Change a Hivemind to Guide Honeybees’ Ecosystem Services?"<br> <br> Dataset for figure 4: The Model replicates Seeleys Choice Experiment (1991).<br> (Shown in the folder: 'Model_Validation_Choice_Empirical','Model_Validation_Choice_Model')<br> Aditionally the accumulated energy [J] (net gain) gathered threw the foraging targets is displayed.<br> <br> Dataset for figure 5: The Model replicates Seeleys Cross Inhibition Experiment (2009).<br> (Shown in the folder: 'Model_Validation_Equilib_Empirical','SModel_Validation_Equilib_Model')<br> Aditionally the accumulated energy [J] (net gain) gathered threw the foraging targets is displayed.<br> <br> Dataset for figure 6: Model data of Seeley's choice experiment (1991) under more natural conditions.<br> The effect of the single parameters and the effect of all parameters together is shown.<br> (Shown in the folder: 'Natural_Foraging_Forager_Size','Natural_Foraging_crop_Load',<br> 'Natural_Foraging_CFS','Natural_Foraging_All_Conditions',)<br> <br> Dataset for figure 7: Model data of Seeley's Cross Inhibition Experiment under more natural conditions.<br> The effect of the single parameters and the effect of all parameters together is shown.<br> (Shown in the folder: 'Natural_Foraging_Equilib_Forager_Size','Natural_Foraging_Equilib_crop_Load',<br> 'Natural_Foraging_Equilib_CFS','Natural_Foraging_Equilib_All_Conditions',)<br> <br> Dataset for figure 8: The influence of waggle dancing robots on the foraging behaviour of honeybees with two different qualities of the foraging targets A and B.<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The waggle dancing robot starts advertising foraging target B when time > 14400 seconds.<br> (The folder shows: 'robo_influence_bad_vs_bad','robo_influence_good_vs_bad','robo_influence_good_vs_good',)</p> <p>Dataset for figure 9: The influence of 10 waggle dancing robots on the foraging behaviour of honeybees with two different qualities of the foraging targets A and B<br> and varrying colony fill status (CFS).<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The 10 waggle dancing robots start advertising foraging target B when time > 14400 seconds. The CFS is varried (0.1, 0.3, 0.5, 0.7, 0.9).<br> (The folder shows: 'robo_influence_bad_vs_bad','robo_influence_good_vs_bad','robo_influence_good_vs_good',)<br> <br> Dataset for figure 10: The effects at the end of the waggle dancing robots on the accumulated energy (through the trips), accumulated pesticides (through trips on foraging target<br> B, where after time>14400 pesticide was sprayed on the foraging target), pollination flights to foraging target A.<br> After 14400 second (12:00) a pesticide is sprayed on the foraging target B under natural conditions.<br> The waggle dancing robot starts advertising foraging target A when time > 14400 seconds.<br> (The folder shows: 'robo_influence_acc_energy','robo_influence_acc_pesticide','robo_influence_pollination')<br> <br> The datasets are contained in the zipped file folder Figures_data.zip.</p>
Human-to-robot Handovers of Cups with Water
<p><em>If you use this dataset in your work, please cite the following publication (accepted to IEEE iROS 2023):</em></p> <p><em>Lastrico, Linda, Nuno Ferreira Duarte, Alessandro Carfí, Francesco Rea, Alessandra Sciutti, Fulvio Mastrogiovanni, and José Santos-Victor. "Expressing and inferring action carefulness in human-to-robot handovers." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9824-9831. IEEE, 2023.</em></p> <p>Setup Description:</p> <p>The experiment is presented as a collaborative task, where the human should help the robot clean the table by handing over the cups, from the rightmost cup to the leftmost, one at a time. The robot receives the cup; in case the cup contains water, it pours the content into the orange bucket, and finally, it places the empty cup in the blue drawer.</p> <p>Participants stand in front of a table with four identical plastic cups placed in a row, equidistant from each other. These cups differ in content, being two empty and two filled with water almost to the brim, constituting two types of objects to be handover: empty or full. Participants faced a Kinova Gen3 robot fixed to a table with two distinct recipients at the robot side. On the left side of the robot, there is an orange bucket meant to contain water, while the blue drawer on the right stores the empty (or emptied) cups. We adopted a within-subject study design where participants are exposed, in a randomized order, to two conditions associated with the controller used by the robot to complete the task: a neutral motion (NEU) and an expressive motion (GAN). The neutral is a simple PID controller and the expressive is Generative Adversarial Network (GAN) which generates robot trajectories that are human-inspired from a previous dataset of humans handing over cups with water and without.</p> <p>Our study involved 15 right-handed participants (8 females, 7 males, average of 26.6 (+-6.2) years old) who provided written informed consent. They were all naive regarding the purpose of the experiments and not directly involved in our research. The self-reported level of knowledge in robotics was: 40.0% professional or advanced, 33.3% average, and 26.7% little or none.<br>360 actions (15 participants x 12 handovers x 2 conditions) were recorded and performed successfully without dropping the cup or spilling the content.</p> <p>Data Description:</p> <p>All the data is synchronize using ROS timestamps.</p> <p>- motion-tracking: Motion Capture data for head, shoulder, and wrist from OptiTrack at 120 Hz + IMU wrist data at 400 Hz. Inside each participant P## folder you will find two other folders P##_neu and P##_gan related to the two interaction conditions where the kinova motion controller changed during the cup pouring and cup placing. Inside each subfolder you will find the following motion tracking data: head.csv, shoulder.csv, wrist.csv, robot.csv (from OptiTrack markers, the robot.csv is the robot's base), imu.csv (from IMU in the wrist), pupil.csv (Pupil ROS node), key.csv (manual labels). All these files have ROS_timestamps that can be used to find the matching frames for each of the sensors. The key.csv are manually picked time flags we marked to define specific moments in the experiment (you can the meaning in the additional notes below).</p> <p>- eye-tracking_#: Pupil-Labs head-mounted eye-trackers at 120Hz for pupil infra-red cameras, and 30 Hz for forward RGB camera. All 16 participants (P##) are present for both robot motion controllers (neutral NEU, and GAN).</p> <p>- go_pro_#: GoPro 1080p video of the size view of the Human-to-robot handovers experiments at 60 Hz. Note that there were 16 participants in this experiment but 3 participants did not give permission to make their image public so we removed the following participants videos: P01, P15, P16.</p> <p> </p>
Hazards&Robots: A Dataset for Visual Anomaly Detection in Robotics
<p>This is the final version of our dataset; we further expand the Corridor scenario.</p> <p>This new version of Corridor includes 20 anomalies and the total frames are 324,408.</p> <p>In this version, we release feature embeddings extracted using a CLIP ViT-B/32 model.</p> <p>This dataset is part of a Data in Brief paper submission.</p> <p>For more information check https://github.com/idsia-robotics/hazard-detection</p> <p> </p>
Supplementary codes and datasets for "Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials"
<p>This repository provides data and codes for manuscript “Wang tiles enable combinatorial design and robot-assisted manufacturing of modular mechanical metamaterials” by M. Doškář, M. Somr, R. Hlůžek, J. Havelka, J. Novák, and J. Zeman, published first as a preprint <a href="https://arxiv.org/abs/2305.09280">arXiv:2305.09280</a> at arXiv.org; see the actual description of the Zenodo entry for the latest reference.</p> <p>This repository contains:</p> <ol> <li>MATLAB and C++ source codes for combinatorial design and numerical analyses (folder <code>./numerics/</code>),</li> <li>experimental data (folder <code>./experiments/</code>),</li> <li>3D models of parts used in robotic-assembly (folder <code>./models/</code>),</li> <li>a control script for robotic assembly (folder <code>./robotics/</code>).</li> </ol> <p><strong>Numerics</strong></p> <p>All simulations were performed with an in-house MATLAB code, which extends the finite element toolbox for finite strain calculations accompanying the work of <a href="https://doi.org/10.1016/j.cma.2020.113333">van Bree, S. E. H. M., Rokoš, O., Peerlings, R. H. J., Doškář, M., & Geers, M. G. D. (2020). A Newton solver for micromorphic computational homogenization enabling multiscale buckling analysis of pattern-transforming metamaterials. Computer Methods in Applied Mechanics and Engineering, 372, 113333</a>. In particular, this snapshot corresponds to a cleaned-up version (excluding files unrelated to the publications) of commit <code>21cfc2e9</code>.</p> <p>The MATLAB codebase contains MEX files written in C++ to accelerate selected procedures. In order to run any code, these MEX files must be compiled first. We use CMake build automation, with the main <code>CMakeLists.txt</code> located in <code>./numerics/mex</code>.</p> <p>Combinatorial search was performed by the <code>RUN_modular_exploration.m</code> script; see definition of problems with the script. The results of the enumerations, stored in <code>./dat/exploration</code>, were analysed with <code>POST_modular_S_v3.m</code>, identifying layouts leading to the extreme (min/max) tilt angles.</p> <p>Comparison against experimental measurements was facilitated by a series of scripts <code>POST_DIC_{...}.m</code>. First, run <code>POST_DIC_step1_extract_points_in_mesh.m</code> to identify locations.mat. Next, post-process extensometer data with <code>POST_DIC_step2_merge_extensometer_data.m</code>, and use <code>POST_DIC_step3_impose_extracted_BC.m</code> to parse DIC results in a format suitable for imposing BC later in this script. Finally, comparison between experimental and computed displacements is provided by <code>POST_DIC_step4_modular_comparison_experiments.m</code>. (Note that the particular files need to be manually provided in the “Compute deformation process” part of <code>POST_DIC_step4_modular_comparison_experiments.m</code>.)</p> <p><strong>Experimental data</strong></p> <p>This folder contains data from (i) an unixaial tension test of a dogbone specimen (both from a MTS loading machine and DIC data) and (ii) two measurement sessions extracting the L-shape domain responses using DIC (<code>20_11_30 - Hluzek_Elka_newassemblyplan</code> and <code>21_04_12 - Hluzek_ Elka_quarters</code> with lower loading threshold). For post-processing, see the above-mentioned <code>POST_DIC_{...}.m</code> scripts. <code>*.mat</code> files present directly in <code>./experiments/</code> folder were obtained and are need by those scripts.</p> <p><strong>3D models</strong></p> <p>The folder contains geometrical models for individual parts needed for robot-assisted assembly of module molds for casting. This includes:</p> <ol> <li>a silo extension to store more tiles (file <code>silo_extension.stl</code>),</li> <li>formwork modules around the main structure for the purpose of casting silicone (file <code>tile_formwork.stl</code>),</li> <li>all types of tiles for the inside structure (file <code>tile_inside_types.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_1.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_2.stl</code>),</li> <li>a spacer shaped for YuMi base to ensure correct distance of the silo and build plate (file <code>yumi_base_3.stl</code>),</li> <li>connection for spacers (file <code>yumi_base_4.stl</code>),</li> <li>spacer holding a silo and the build plate (file <code>yumi_base_5.stl</code>),</li> <li>YuMi grippers with extensions to hold the tiles (file <code>grippers_extend.st</code>).</li> </ol> <p><strong>Robotics</strong></p> <p>The folder contains a single file with a script created in RobotStudio (RobotWare Version: 6.08.01.00, SmartGripper Version: 3.55.0000.00) to assemble the plan with YuMi IRB 14000-0.5/0.5 left hand.</p> <p><strong>Acknowledgement</strong></p> <p>The related research, experiments, and code development were supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
METRIC - Multi-Eye To Robot Indoor Calibration Dataset
<p>The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.</p>
Benchmark movement data set for trust assessment in human robot collaboration
<p>In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016).</p> <p>Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants, 7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens:</p> <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> <p>The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are:</p> <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> <p>This is followed by the trust questionaire items:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>The last two columns are</p> <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>
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.