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zenodo52/100

RoHuCAD: Robots and Humans Collaborative Anomaly Detection

<h1>RoHuCAD: Robots and Humans Collaborative Anomaly Detection</h1> RoHuCAD is a dataset of human-robot collaboration in a robotic workshop (check <code>workshop_layout.png</code>). Two robots (collaborative manipulator - cobot, autonomous mobile robot - AMR) assist three human operators in assembly of electronic devices. There are two 8-min long recordings in the dataset. They mostly follow the same scenario, with slightly different anomalies. The data is in ROS Noetic rosbag format. <h2>Included data </h2> <ul> <li>RGBD camera data (color + depth) <ul> <li>3 cameras: <a href="https://www.intelrealsense.com/depth-camera-d435i/">Intel Realsense D435i</a></li> <li>color and depth data at 6 frames per second</li> <li>Intrinsic calibration data</li> <li>Extrinsic calibration data (positions and orientations)</li> </ul> </li> <li>Information about positions of robots <ul> <li>AMR: <a href="https://www.ez-wheel.com/en/development-kit-for-agv-and-amr">Ez-Wheel SWD® Starter Kit</a></li> <li>Cobot: <a href="https://www.universal-robots.com/products/ur10-robot/">Universal Robots UR10e</a></li> </ul> </li> </ul> <h2>Annotations</h2> Annotations of specific anomalies are included (CSV file with columns: event_id, tstart, tend, event_type, person_id, camera_id) <ul> <li>Gestures / poses <ul> <li>BENT</li> <li>T-POSE (hands horizontally to the sides)</li> <li>L+R-UP (both hands up)</li> <li>RH-UP (right hand up)</li> <li>LH-UP (left hand up)</li> <li>SQUAT</li> <li>HI-POSE (waving)</li> </ul> </li> <li>Unsafe behaviour <ul> <li>Human in robot working area</li> <li>Standing back to (moving) robot</li> <li>Looking at phone</li> <li>Human in the way of AMR</li> </ul> </li> <li>Normal activities <ul> <li>Assembling/Working</li> <li>Loading/unloading AMR</li> </ul> </li> </ul> <h2>ROS topics</h2> <ul> <li><code>/tf </code></li> <li><code>/tf_static</code></li> <li><code>/joint_states</code></li> <li>cam_ws2_box <ul> <li><code>/cam_ws2_box/color/camera_info</code></li> <li><code>/cam_ws2_box/color/image_raw/compressed</code></li> <li><code>/cam_ws2_box/depth_registered/camera_info</code></li> <li><code>/cam_ws2_box/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta2_ws2 <ul> <li><code>/cam_ta2_ws2/color/camera_info</code></li> <li><code>/cam_ta2_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta2_ws2/depth_registered/camera_info</code></li> <li><code>/cam_ta2_ws2/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta1_ws2 <ul> <li><code>/cam_ta1_ws2/color/camera_info</code></li> <li><code>/cam_ta1_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/camera_info</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/image_raw</code></li> </ul> </li> </ul> <h2>Acknowledgement</h2> The work leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme within the ULTIMATE project under the Grant Agreement no 101070162.

opencc-by-4.0Nov 2024View details →

zenodo52/100

Labeled Time Series Data of Force/Torque for Monitoring Assembly Processes with a Delta Robot

This dataset comprises 524 recordings of 6-dimensional time series data, capturing forces in three directions and torques in three directions during the assembly of small car model wheels. The data was collected using an equidistant sampling method with a sampling period of 0.004 seconds. Each time series represents the process of assembling one wheel, specifically the placement of a tire onto a rim, and includes a label indicating whether the assembly was successful (OK). The wheels were assembled in batches of four, and the recordings were obtained over six different days. The labels of recordings from two (days 3 and 4) of the six days are invalid as described in [1].  The labels presented in this data set are only binary (they do not describe the reason of the failure). The labels of recordings from days 5 and 6 are created by human while the other labels came from a convolutional neural network based computer vision classifier and can be inaccurate as described in section 5.4 of [1].    <h4>Dataset Structure:</h4> <ul> <li>File: <code>ForceTorqueTimeSeries.csv</code> <ul> <li>Columns: <ul> <li><code>idx (1-524)</code>: Index of the recording corresponding to the assembly of one wheel.</li> <li><code>label (true/false)</code>: Indicates whether the assembly was successful (TRUE = product is OK).</li> <li><code>meas_id (1-6)</code>: Identifier for the day on which the recording was made (refer to Table 2.1 in [1]).</li> <li><code>force_x</code>: X-component of the force measured by the sensor mounted on the delta robot's end effector.</li> <li><code>force_y</code>: Y-component of the force.</li> <li><code>force_z</code>: Z-component of the force.</li> <li><code>torque_x</code>: X-component of the torque.</li> <li><code>torque_y</code>: Y-component of the torque.</li> <li><code>torque_z</code>: Z-component of the torque.</li> </ul> </li> </ul> </li> </ul> <h4>Additional Files:</h4> <ul> <li><code>IMG_3351.MOV</code>: A video demonstrating the assembly process for one batch of four wheels.</li> <li><code>F3-BP-2024-Trna-Ales-Ales Trna - 2024 - Anomaly detection in robotic assembly process using force and torque sensors.pdf</code>: Bachelor thesis [1] detailing the dataset and preliminary experiments on fault detection.</li> <li><code>F3-BP-2024-Hanzlik-Vojtech-Anomaly_Detection_Bachelors_Thesis.pdf</code>: Bachelor thesis [2] describing the data acquisition process.</li> </ul> <h3>References:</h3> <ol> <li>Trna, A. (2024). Anomaly detection in robotic assembly process using force and torque sensors [Bachelor’s thesis, Czech Technical University in Prague].</li> <li>Hanzlik, V. (2024). Edge AI integration for anomaly detection in assembly using Delta robot [Bachelor’s thesis, Czech Technical University in Prague].</li> </ol>

opencc-by-4.0Sep 2024View details →

zenodo52/100

PE-HRI-temporal: A Multimodal Temporal Dataset in a robot mediated Collaborative Educational Setting

Please note that this dataset corresponds to the training data used in "Social robots as skilled ignorant peers for supporting learning "[7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).    This data set consists of multi-modal temporal team behaviors as well as learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore evolution of log actions, speech behavior, affective states, and gaze patterns for students to model constructs such as engagement, motivation, collaboration, etc. in educational settings.  In this data set, team level data is collected from 34 teams of two (68 children) where the children are aged between 9 and 12. There are two files:   PE-HRI_learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:  <ul> <li> last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.  </li> <li> T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available. </li> <li> T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.  </li> <li> T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity </li> </ul> PE-HRI_behavioral_timeseries_w_labels.csv: In this file, for each team, the interaction of around 20-25 minutes is organized in windows of 10 seconds; hence, we have a total of 5048 windows of 10 seconds each. We report team level log actions, speech behavior, affective states, and gaze patterns for each window. More specifically, within each window, 26 features are generated in two ways:  <ol> <li>non-incremental</li> <li>incremental</li> </ol> A non-incremental type would mean the value of a feature in that particular time window while an incremental type would mean the value of a feature until that particular time window. The incremental type is indicated by an "_inc" at the end of the feature name. Hence, in the end, within each window, we have 52 values:  <ul> <li> T_add/(_inc): The number of times a team added an edge on the map in that window/(until that window). </li> <li> T_remove/(_inc): The number of times a team removed an edge from the map in that window/(until that window). </li> <li> T_ratio_add_rem/(_inc): The ratio of addition of edges over deletion of edges by a team in that window/(until that window). </li> <li> T_action/(_inc): The total number of actions taken by a team (add, delete, submit, presses on the screen) in that window/(until that window). </li> <li> T_hist/(_inc): The number of times a team opened the sub-window with history of their previous solutions in that window/(until that window). </li> <li> T_help/(_inc): The number of times a team opened the instructions manual in that window/(until that window). Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.  </li> <li> T1_T1_rem/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I add an edge, I then delete it in that window/(until that window). </li> <li> T1_T1_add/(_inc): The number of times either of the two members in the team followed the pattern consecutively: I delete an edge, I add it back in that window/(until that window). </li> <li> T1_T2_rem/(_inc): The number of times the members of the team followed the pattern consecutively: I add an edge, you then delete it in that window/(until that window). </li> <li> T1_T2_add/(_inc): The number of times the members of the team followed the pattern consecutively: I delete an edge, you add it back in that window/(until that window). </li> <li> redundant_exist/(_inc): The number of times the team had redundant edges in their map in that window/(until that window). </li> <li> positive_valence/(_inc): The average value of positive valence for the team in that window/(until that window). </li> <li> negative_valence/(_inc): The average value of negative valence for the team in that window/(until that window). </li> <li> difference_in_valence/(_inc): The difference of the average value of positive and negative valence for the team in that window/(until that window). </li> <li> arousal/(_inc): The average value of arousal for the team in that window/(until that window). </li> <li> gaze_at_partner/(_inc): The average of the the two team member's gaze when looking at their partner in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_robot/(_inc): The average of the the two team member's gaze when looking at the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_other/(_inc): The average of the the two team member's gaze when looking in the direction opposite to the robot in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_left/(_inc): The average of the the two team member's gaze when looking at the left side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> gaze_at_screen_right/(_inc): The average of the the two team member's gaze when looking at the right side of the screen in that window/(until that window). Each individual member's gaze is calculated as a percentage of time in that window/(until that window).  </li> <li> T_speech_activity/(_inc): The average of the two team member's speech activity in that window/(until that window). Each individual member's speech activity is calculated as a percentage of time that they are speaking in that window/(until that window).  </li> <li> T_silence/(_inc): The average of the two team member's silence in that window/(until that window). Each individual member's silence is calculated as a percentage of time in that window/(until that window).  </li> <li> T_short_pauses/(_inc): The average of the two team member's short pauses over their speech activity in that window/(until that window). Each individual member's short pause refers to a brief pause of 0.15 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_long_pauses/(_inc): The average of the two team members long pauses over their speech activity in that window/(until that window). Each individual member's long pause refers to a pause of 1.5 seconds and is calculated as a percentage of time in that window/(until that window).  </li> <li> T_overlap/(_inc): The average percentage of time the speech of the team members overlaps in that window/(until that window). </li> <li> T_overlap_to_speech_ratio/(_inc): The ratio of the speech overlap over the speech activity of the team in that window/(until that window). </li> </ul> Apart from these 52 values, within each window, we also indicate:  <ul> <li>team: The team to which the window belongs to.</li> <li>time_in_secs: Time in seconds until that window.</li> <li>window: The window number.</li> <li>normalized_time: The time when this window occurred with respect to the total duration of the task for a particular team. </li> <li>cluster_labels: The cluster number associated with each time window in reference to the productive and non-productive clusters found in [3]</li> <li>PE_score: The Productive Engagement score in each window</li> </ul> Lastly, we briefly elaborate on how the features are operationalised. We extract log behaviors from the recorded rosbags while the behaviors related to both gaze and affective states are computed through the open source library OpenFace [6] that returns both facial actions units (AUs) as well as gaze angles. For voice activity detection (VAD), that classifies if a piece of audio is voiced or unvoiced, we made use of the python wrapper for the open source Google WebRTC VAD. The literature that inspired our log, audio and video features as well as the tools used to extract them are described in more detail in [3,4]. However, in those papers, we make use of only the aggregate version of this data [5]. Please note that this dataset corresponds to the training data used in [7]. This (second) version of the dataset additionally includes labels (PE score and cluster labels for each datapoint).

opencc-by-4.0Oct 2021View details →

zenodo48/100

Visuo-motor dataset recorded from a micro-farming robot

This is the accompanying dataset of the paper [1] describing algorithms for intrinsic motivation and episodic memory on the Sony LettuceThink microfarming robot. The LettuceThink microfarming robot developed by Sony Computer Science Laboratories consists of an aluminium frame with an X-Carve CNC machine mounted on it. The CNC machine is used to provide 3-axes movements to a depth camera (Sony DepthSense) mounted at the tip of the vertical z-axis (the end-effector camera). In the experiments presented in the paper, the end-effector camera is facing top-down and only two motors are used (x and y). A simulator of the LettuceThink robot has been developed to ease the testing of different configurations of the learning system. The simulator generates sensorimotor data from requested trajectories of the end-effector camera. Knowing the initial position of the CNC machine and the target position, the simulator linearly interpolates the trajectory and returns the intermediate positions of the camera together with the images captured from each specific position. The sensorimotor data returned by the simulator have been prerecorded by performing a full scan of the (x,y) plane of the CNC machine using a resolution of 5mm. This resulted in 24,964 images, each mapped to an (x,y) position of the CNC machine. The dataset published here contains these images. In particular, the dataset consists of a set of images, each named with the specific position of the 2 motors of the robot. A python script for generating visuo-motor trajectories (sequences of data consisting of [image, motor_x, motor_y]) from this dataset is available at the following github page: <a href="https://github.com/guidoschillaci/sonylettucethink_dataset">https://github.com/guidoschillaci/sonylettucethink_dataset</a> Provided with the dataset is also a python script that allows to easily read the images and to generate trajectories (returning This work has been supported by the EU-H2020 ROMI Project and by the EU-H2020 Marie Sklodowska Curie project "Predictive Robots" (grant agreement no. 838861)References: [1] Schillaci, G., Villalpando, A. P., Hafner, V. V., Hanappe, P., Colliaux, D., & Wintz, T. (2020). Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces. arXiv preprint arXiv:2001.01982.

opencc-by-4.0Feb 2020View details →

zenodo48/100

Experimental data for the motor learning study performed: "Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks"

The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. The details of the study are described in [doi: 10.3389/fnins.2020.600059]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses are stored as a “csv” file. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.

opencc-by-4.0Dec 2020View details →

zenodo48/100

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Oct 2023View details →

zenodo48/100

Robot@Home2, a robotic dataset of home environments

The Robot-at-Home dataset (Robot@Home, paper <a href="http://mapir.uma.es/papersrepo/2017/2017-raul-IJRR-Robot_at_home_dataset.pdf">here</a>) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms. This dataset is unique in three aspects: <ul> <li>The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner.</li> <li>It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms.</li> <li>The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences</li> </ul> During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms. Robot@Home2 <a href="https://github.com/goyoambrosio/RobotAtHome2">Robot@Home2</a>, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a <a href="#bottom">relational database</a> that states the contextual information and data links, compatible with Standard Query Language. Secondly,a <a href="https://pypi.org/project/robotathome/">Python package</a> for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of <a href="https://drive.google.com/drive/folders/1ENnxbKP5MJdlGl2Q93WTbIlofuy6Icxq">Jupyter notebooks</a>, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics. If you use Robot@Home2, please cite the following <a href="https://www.sciencedirect.com/science/article/pii/S2352711023001863">paper</a>: Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, <a href="https://doi.org/10.1016/j.softx.2023.101490">https://doi.org/10.1016/j.softx.2023.101490</a>. <blockquote>@article{ambrosio2023robotathome2, title = {The Robot@Home2 dataset: A new release with improved usability tools}, author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez}, journal = {SoftwareX}, volume = {23}, pages = {101490}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101490}, url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863}, keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab} }  </blockquote> Version history <a href="../record/3901564">v1.0.1</a> Fixed minor bugs. <a href="../record/4495821">v1.0.2</a>  Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version. <a href="../record/4499043">v2.0.0</a> SQL based dataset. Robot@Home v1.0.2 has been packed into a <a href="https://www.sqlite.org/index.html">sqlite</a> database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the <a href="https://github.com/goyoambrosio/RobotAtHome2">toolbox</a>. <a href="../record/4530453">v2.0.1</a> A forgotten foreign key pair have been added. <a href="../records/7811783">v.2.0.2</a> The views have been consolidated as tables which allows a considerable improvement in access time. <a href="../records/7811795">v.2.0.3</a> The previous version does not include the database. In this version the database has been uploaded. v.2.1.0 Depth images have been updated to 16-bit. Additionally, both the RGB images and the depth images are oriented in the original camera format, i.e. landscape.

opencc-by-4.0Jul 2023View details →

zenodo48/100

EEG Data for Emotive Response to Robot Facial Expressions

This dataset consists of EEG recorded during visual human-robot interaction from 10 healthy participants to investigate the emotive response in EEG to different robot facial expressions. Participants observed four different facial expressions (angry, happy, sad and surprised along with neutral expression) displayed by the social robot Miko on its digital screen. EEG was recorded from 16 unipolar channels in frontal, central, temporal, parietal, and occipital locations . During each trial, an emotion stimulus was displayed for approximately 4s followed by 4s break during which the Miko robot displayed neutral expression and blinked regularly. Emotions were displayed in random order. Total of 240 EEG trials were recorded from each participant with 60 trials per emotion. The dataset provides raw minimally filtered EEG along with cleaned EEG with artefacts removal using ICA with sampling frequency of 128 Hz, and corresponding stimulus onset markers. Please refer to README file for further details and example code. Please cite the original publication: M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," IEEE Internet of Things Journal, DOI: <a href="https://doi.org/10.1109/JIOT.2021.3097592">10.1109/JIOT.2021.3097592</a>. Preprint:   M. Wairagkar et al., "Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots," <a href="https://arxiv.org/abs/2012.04511">arXiv:2012.04511</a>

opencc-by-4.0Dec 2021View details →

zenodo48/100

Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"

The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.

opencc-by-4.0Jul 2021View details →

zenodo48/100

Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data

This is the data produced in the course of selecting relevant literature for the "User Acceptance in Human-Robot Interaction" literature review article. Contents: <ul> <li>Initial pass records: prisma0_wos.xlsx + prisma0_scopus.xlsx</li> <li>Initial pass eligibility assessment: prisma0_eval.xlsx</li> <li>Second pass records, filtering and coarse assessment: prisma1.xlsx</li> <li>Third pass records, filtering and coarse assessment: prisma2.xlsx</li> <li>Fine eligibility assessment of 2nd and 3rd pass: prisma_avalanche_1_and_2_report_update_04_26.pdf</li> </ul>

opencc-by-4.0Jul 2024View details →

zenodo48/100

Data on robotic grinding of Inconel 718 part with 3M Cubitron II 984F belt for tool wear and material removal analysis

Data on robotic grinding of Inconel 718 part for tool wear and material removal analysis Date data was obtained: May 2019 The performance of a metal grinding operation with a robot has been studied, specifically how the grinding capability changes as time goes by and the tool gets worn. A pneumatic grinding tool has been implemented on the robot flange and abrasive belts of 3M Cubitron II 984F have been used. A rectangular metallic part of known dimensions has been attached to a load cell and has been grinded several times in consecutive tests. The grinding operation consists on a straight line along the longest side of the part. During each test session, the same abrasive belt was used with fixed grinding conditions (tool angle, applied force, overlap, robot feed), until the grinding time reached 20 minutes. The metal part has been weighed at regular intervals with the load cell, which allowed us to measure the evolution of the removed height of material for each pass, depending on grinding time. The quantity of grinded material is measured as the height reduction in the part as the robot moves over the part at certain speed. Test sessions were designed for 4 tool angles (25, 35, 45, 75º related to the vertical), and  were repeated three times for each angle. With 75º the tool was almost horizontal and it provided the smallest material removal capability. With 25º the tool was almost perpendicular to the area being grinded and it provided the highest material removal capability.  The data of the tests is presented in the following units: - Time: seconds: - Removed material height per grinding tool pass: millimeters. The user of the data may easily convert the removed material height per pass into removed material volume or weight per pass. Considering that the width of the grinding belt is 12.5 mm, and the length of the grinded tool is 160 mm, if the height of the removed material is multiplied to the length and width the removed volume per pass can be calculated. Multiplying the volume with the density the weight of the removed material per pass can be calculated. The results of different tests presented in the .xlsx document, which can be accessed using free software such as OpenOffice or LibreOffice: https://www.openoffice.org https://www.libreoffice.org/ TECHNICAL DESCRIPTION OF THE USED DEVICES AND CONDITIONS - Material of the grinded part: Inconel 718, density 8.19g/cm3.  - Dimensions of the grinded part: 160x90x40 mm. - Robot: Stäubli TX90L. - Belt grinding tool: AMTRU SwingBelt 120. https://www.amtru.com - Applied pneumatic pressure on the grinding tool: 9 bars. - Belt speed: Maximum possible speed obtained with 7 bars pneumatic mains in the workshop. - Abrasive belt: 3M Cubitron II 984F, 610x12.5 mm, 36 grit (roughing). - Load cell to measure the weight of the part: HBM SP4M, capacity 3 kg, precision 0.01 g. - Overlap (tool lateral displacement between two passes): 6.25 mm. - Robot feed: 75 mm/s (100 mm/s for the 45º test). IDEKO Research Centre Address:      Arriaga kalea 2     20870, Elgoibar, SPAIN Contact:     Asier Barrios, abarrios@ideko.es     Patxi Hacala, phacala@ideko.es     Phone: (+34) 943 74 80 00

opencc-by-4.0Sep 2019View details →

zenodo48/100

PEDRo: an Event-based Dataset for Person Detection in Robotics

This event-based dataset is specifically designed for person detection in robotics applications.

opencc-by-4.0Apr 2023View details →

zenodo48/100

Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots

<h1>Dataset and code description</h1> This repository contains the codes and data for theScience Robotics paper Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots. The codes are in rr_scirob_analyses and the datasets are in rr_scirob_data.  If you want to rerun the data processing as presented in the paper, you need both rr_scirob_analyses and rr_scirob_data. You can copy the contents of rr_scirob_data into rr_scirob_analyses, as they have the same folder structure. Alternatively, you can run the download scripts to obtain the partial datasets relevant for certain subfigures. The file rr_scirob_data_readmes contains more detailed README files (rosbag info). You can copy its contents to rr_scirob_analyses after copying the contents of the rr_scirob_data. The individual datasets are organised into seven folders. <h2>Three Figures with Key Behavioural Metrics </h2> Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are: <ul> <li>Figure-2-KBM-1-Queen                     Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers                  Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb                      Comb and Brood -related Key Behavioural Metrics </li> </ul> Each of these Figure-X folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure.  These are macro, micro, mezo, social, related to the four panels of that figure. Each of these subfolders contains a README file, describing how to process the data and providing further details.  Furthermore, there are three additional folders located in each of the 'panel' folder: <ul> <li>data: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e.,  https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li>tmp: This folder is used to store intermediate results of the processing scripts</li> <li> output: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper. <h2>One Resting Analysis Figure</h2> One folder corresponds to the queen resting analysis figure <ul> <li>Figure-5-Resting       : Queen resting time analysis</li> </ul> This folder has three subfolders named data, tmp and output similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case. <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> Three other folders are related to performance analysis of the core methods required to calculate the KBMs. <ul> <li>KBM-1-performance evaluation:       Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation:       Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation:       Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results. <h2>Additional materials and data</h2> The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file 2023-month-queenpos-short.txt.  A description of the file structure is provided in the README of the relevant KBM folder. Additional data are available in the dataset section of https://roboroyale.eu. <h2>Rosbags</h2> The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system: <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag       -   queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag           -   queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag              -    worker bee trophylaxis  (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag           -   worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please cite the relevant paper, i.e.,  Ulrich, Stefanec, Rekabi-bana et al.: Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots. Science Robotics, 2024.

opencc-by-sa-4.0Oct 2024View details →

zenodo48/100

The relevance of signal timing in human-robot collaborative manipulation

Dataset version 1.0.1. The data collected here are attached to the following journal article: F. Cini*, T. Banfi*, G. Ciuti, L. Craighero, M. Controzzi, The relevance of signal timing in human-robot collaborative manipulation. Science Robotics Vol. 6 Issue 58, 2021. DOI: 10.1126/scirobotics.abg1308 To achieve a seamless human-robot collaboration, it is crucial that robots express their intentions without perturbating or interrupting the task that a human partner is performing at that moment. Although it has not received much attention so far, this issue is important when robots assist humans in physical and manipulation tasks. The main question addressed here is whether there is a more appropriate time to inform a human partner that a robot is requesting to pass them an object. This question is posed in a reference scenario where human individuals are involved in a continuous pick-and-place task that cannot be interrupted. Our findings showed that providing a cue at the beginning of a reach-to-grasp movement could severely interfere with the ongoing human action, increasing the number of errors made by humans, slowing down and degrading the smoothness of their arm movement, and deflecting their gaze. These disruptive interferences strongly decreased, until they disappeared, when the robot provided the cue to the human partners shortly after the participants picked up an object, identifying this as the best signaling timing. The results of this work showed how the signaling timing may have a decisive influence on the performances of the human-robot teamwork and contribute to understating the mechanisms underpinning the phenomenon of cognitive-motor interference in humans.

opencc-by-4.0Aug 2021View details →

zenodo48/100

Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies

Data published with the manuscript: “Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies”. 2021 (Paez-Granados & Billard, 2021) Summary: This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas. The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI). Instructions:  This dataset contains the following main files: <ol> <li>Data Description.pdf: Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li>collision_test_rawdata.zip:  This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2. ‘test_name’/01_values/’testName’_CFC1000.xlsx</li> <li>collision_test_analysis.zip: This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> Dataset with filtered sensor data accordingly to SAEJ21.</li> <li>collision_data_matlab_structure.zip: Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</li> <li>scripts-crash-test-service-robots.zip: processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></li> </ol>

opencc-by-4.0Aug 2021View details →

zenodo48/100

Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"

This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper: Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022). <a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>. The scripts were tested on Matlab R2021a on Windows. Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named plot<...>.m that recreate the actual plots. Some folders also have a scripts analyze<...>.m to analyze the data; these need to be run before the actual plotting. For more details, please see the paper.

opencc-by-4.0Jun 2022View details →

zenodo48/100

THÖR-Magni (Demo Subset): a new multi-modal context-rich dataset of human-robot motion

The Magni Human Motion Dataset provides high-quality tracking information from motion capture, eye-gaze trackers, and on-board robot sensors in a semantically rich environment. To induce natural behavior of recorded participants, we utilized loosely scripted task assignment, which induced participants to navigate through a dynamic laboratory environment in a natural and purposeful way. The dataset sets a high-quality standard as realistic and accurate data is enhanced with semantic information, enabling development of new algorithms that rely not only on tracking information but also on contextual cues of moving agents, static and dynamic environments.   Link to dashboard that uses the data: https://magni-dash.streamlit.app/ Here we publish a subset of the final dataset, to accompany the presentation at the 2023 IEEE International Conference on Robotics and Automation (ICRA)

opencc-by-4.0May 2023View details →

zenodo44/100

Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"

This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), Science Robotics, 4(28), doi: 10.1126/scirobotics.aau7897 Contents:  <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> See the readme and summary files contained within the archives for further details.

opencc-by-4.0Mar 2019View details →

zenodo44/100

Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)

These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing). Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.

opencc-zeroApr 2015View details →

zenodo44/100

Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the upper limb joints in planar robot-aided therapies

These files contain the raw data (acquired from different users) necessary to perform the algorithm introduced in the scientific paper: PAPER: Kinematic reconstruction of the upper limb joints in planar robot-aided therapies Authors: Arturo Bertomeu-Motos, Ricardo Morales, Jorge A. Díez, Luis D. Lledó, Francisco J. Badesa, Nicolas Garcia-Aracil Conference: ICORR 2015, IEEE 14th International Conference on Rehabilitation Robotics, August 2015 All the orientations are expressed regarding the origin of the robot. a) Robot Joints: Planar robot joints acquired during the experiment, in radians (j1-j3 columns). This robot is referenced in the paper. b) Quaternion IMU shoulder: Unit quatenion acquired through a 9DoFs Inertial Measurement Unit (IMU) developed by Shimmer (qw1-qz columns). c) Upper arm acceleration: Acceleration acquired from a 3-axial accelerometer developed by Shimmer (X-Z columns). It is normalized regarding the gravity (9.81m/s^2). d) Quaternion Tracker onto Shoulder: unit quaternion of the tracker placed onto the shoulder acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns). e) Quaternion Tracker onto Upper Arm: unit quaternion of the tracker placed onto the upper arm acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).

opencc-zeroApr 2016View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record