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124 results for “robotic dataset”
Datasets for article "Robot Self-Calibration Using Actuated 3D Sensors"
<p>Real and sythetic datasets used in artilcle "Robot Self-Calibration Using Actuated 3D Sensors". For each recodring of a calibration scene is there is a ROS bag file holding a single message of type vision_3d_msgs/Actuated3dRecording.</p>
Robotic Monitoring of Dunes: a dataset from the EU habitats 2110 and 2120 in Sardinia (Italy)
<p>Data collected between the 16th and the 19th of May 2022, in Platamona, 07037 (SS), Sardinia, Italy, within the Natura 2000 SAC ITB010003. The data acquisition has been conducted by a team composed of both robotic engineers and plant scientists. The platform used to collect the data is the ANYmal C quadrupedal robot. </p> <p>The dataset contains three different sets of data: <br> 1) species data - pictures and videos of three different typical species of the habitat 2110 and 2120 and one alien species.<br> 2) 3D mapping data - robot status and point cloud<br> 3) monitoring mission data - robot status and pictures and videos taken by the robot during the autonomous surveys.</p> <p>This dataset has a multidisciplinary scope and can be used by researchers in several fields. For instance, point clouds and information about the robot state could be used by robotic engineers to test or validate their own methods as well as benchmark the robot performance. On the other hand, plant videos and images recorded by the robot could be used by botanists to assess the quality of this information as well as the habitat's conditions, or by computer scientists interested in testing their AI algorithms for species detection and classification.</p>
Robotic Monitoring of Forests: a Dataset from the EU habitat 9210* in the Tuscan Apennines (Central Italy)
<p>Data collected between the 27th and the 28th of April 2022, in Chiusi Della Verna, Arezzo 52010 (AR), Italy, inside the Natura 2000 SAC IT5180101. The data has been acquired mainly by the legged robot ANYmal C guided by a team of both roboticists and plant scientists. </p><p>The dataset contains four different sets of data: </p><p>1) species data - photos of four indicator species of the habitat 9210 (3 typical species and 1 early warning species).</p><p>2) mapping data - three dimensional point clouds of the habitat environment.</p><p>3) autonomous monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the autonomous mission.</p><p>4) teleoperated monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the teleoperated mission.</p><p>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. On the one hand, robotic engineers could, for instance, benchmark the performance of the robots and test or validate their own methods using the point clouds and the information about the robot state. On the other hand, botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant videos and images that the robot captured, or computer scientists could test their AI algorithms for identifying and classifying different species using these data.</p>
Robotic manipulation datasets for offline compositional reinforcement learning
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Datasets and images of publication: Self-healing and high interfacial strength in multi-material soft pneumatic robots via reversible Diels-Alder bonds
<p>Data and Figures of the publication:</p> <p>Terryn, S.; Roels, E.; Brancart, J.; Assche, G.V.; Vanderborght, B. Self-Healing and High Interfacial Strength in Multi-Material Soft Pneumatic Robots via Reversible Diels–Alder Bonds. <em>Actuators</em> <strong>2020</strong>, <em>9</em>, 34.</p>
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
Postural Optimization for a Safe and Comfortable Human-Robot Interaction: Experiment Dataset
<p>In human-robot collaboration the robot's behavior impacts the worker's safety, comfort, and his acceptance of the robotic system. In this paper we address the problem of how to improve the worker's posture during human-robot collaboration. Using postural assessment techniques, and a personalized human kinematic model, we optimize the model body posture to fulfill a task while avoiding uncomfortable or unsafe postures. We then derive a robotic behavior that leads the worker towards that improved posture. We validate our approach in an experiment involving a joint task with 39 human subjects and a Baxter torso-humanoid robot.</p> <p>This repository contains the anonymized recorded data of our experiment. For all the subjects, we have included their recorded posture, using a motion capture system, and a video taken from a camera located on the robot head. Each data is divided by subjects and by tested conditions. In a separate file, we also include the result of the survey answered alongside the experiment.</p>
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment
<p>This repository contains all needed data sets for the contribution in Journal of Sensors and Sensor Systems "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div> title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div> volume = {14},</div> <div> issn = {2194-8771},</div> <div> shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div> url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div> doi = {10.5194/jsss-14-37-2025},</div> <div> abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div> language = {English},</div> <div> number = {1},</div> <div> journal = {Journal of Sensors and Sensor Systems},</div> <div> author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div> month = feb,</div> <div> year = {2025},</div> <div> note = {Publisher: Copernicus GmbH},</div> <div> pages = {37--46},</div> <div>}</div> </div>
Mari4_YARD - Collabortive Robots - Dataset
<div> <div>Welcome to the Plasma Cut App with collaborative robot Demonstration Results Dataset, a unique collection of data showcasing the information needed for localizing the robot and performing an automatic cut in the target structure. This dataset is designed to facilitate research in robotics and perception, particularly in the areas of collaborative application.This dataset is generated during the demonstration of the projection technology at NODOSA and AIMEN facilities during technology demonstrations.</div> </div>
Tiny Robotics Dataset and Benchmark for Continual Object Detection
<p>Dataset for <strong>TiROD</strong>: Tiny Robotics Dataset and Benchmark for Continual Object Detection<br><br>Official Website -> <a href="https://pastifra.github.io/TiROD/">https://pastifra.github.io/TiROD/</a></p> <p>Code -> <a href="https://github.com/pastifra/TiROD_code">https://github.com/pastifra/TiROD_code</a></p> <p>Video -> <a href="https://www.youtube.com/watch?v=e76m3ol1i4I">https://www.youtube.com/watch?v=e76m3ol1i4I</a></p> <p>Paper -> <a href="https://arxiv.org/abs/2409.16215">https://arxiv.org/abs/2409.16215</a></p>
Dataset - Swarm of Micro Flying Robots in the Wild
<p>Dataset for manuscripts "Swarm of Micro Flying Robots in the Wild".</p> <p>The file "data_benchmark.zip" contains data files of the simulation and real-world experiments of the manuscript: "Swarm of Micro Flying Robots in the Wild". And it also contains MATLAB scripts to recreate the plots and graphs as presented in the manuscript.<br> Please see [data_out/ReadMe.txt] for code usage.</p> <p>The file "hardware.zip" contains PCB files and mechanical drawings of our micro flying robots.</p> <p>The file "realworldflight_software.zip" contains the source code of object detection and localization drift correction used in real-world experiments.<br> </p> <p> </p>
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>
Ammonite Robot Dataset
<p>Dataset for ammonite robotics project. This dataset is comprised of a .zip folder containing: 1) the Arduino code uploaded to the robot microcontroller, 2) sample footage of the movement for each biomimetic robot during 3D motion tracking, and 3) 3D models in .stl format for each biomimetic robot (serpenticone, oxycone, sphaerocone, and morphospace center). Each robot morphotype has their own file including models of: both batteries (3.7V and 7.4V), counterweights cast with bismuth, the electronics cartridge, electronic components (microcontroller, charger/regulator, motor driver, wires, LED indicator, and IR sensor), the PETG impeller, chamber liquid and water pump liquid (LiquidALL.stl), a brushed DC motor, PETG parts 1-4, PETG lid, self-healing rubber valve, and the water displaced by the external model. The PETG parts were printed in natural colored PETG with solid infill and 0.12 mm vertical resolution.</p>
Video-Trajectory Robot Dataset
<p>This dataset consists of color and depth videos of Panda robot motions and their corresponding joint and Cartesian trajectories. The dataset also includes the trajectories of a receiver robot for the purpose of an object handover. Each motion sample comprises 6 files (RGB video, depth video and 4 giver/receiver trajectories in time series form). Total number of motion samples: 38393.</p> <p>Structure: MPEG-4 videos of robot motion and corresponding Python serialized (or “pickled”) files, containing joint and Cartesian trajectories. Dataset is divided into four parts: simulation dataset (PandaHandover_Sim.zip), real train dataset (PandaHandover_Real_Train.zip), real validation dataset (PandaHandover_Real_Val.zip), real test dataset (PandaHandover_Real_Test.zip). Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .pkl files can be loaded using Python (>=3.7) and the Python library Pandas (>=1.1.3).</p>
Dataset - Literature on service robots in the hospitality industry
<p>List of fifty-nine articles retrieved from Web of Science, Google Scholar, and Scopus databases upon search inquiries with "robot," "hotel," and "hospitality" keywords. Data collection between October 2021 and March 2022. Figures of analysis in three clusters: robot*-customer relationship, robot-employee relationship, and robot-firm relationship. </p>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized
<p>Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.</p> <p>The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.</p> <p><strong>Exchanges</strong> file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange. </p> <p><strong>Overall </strong>file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.</p> <p>The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns. </p> <p><em>Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.</em></p> <p>Anonymization macros:</p> <p>[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.</p> <p>[NAME_REMOVED]: A name of another person mentioned by the participant.</p> <p>[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.</p> <p>[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.</p> <p>[AGE_REMOVED]: Participant's or other person's age.</p> <p>[INFORMATION_REMOVED]: Sensitive information shared by the participant.</p> <p>[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.</p>
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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.