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2,639 results for “Robotic”
Brain-inspired multimodal hybrid neural network for robot place recognition
<p>Brain-inspired multimodal hybrid neural network for robot place recognition</p>
Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks (Video)
<p>Supplementary video for the paper "Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks".</p>
Resources for the article "Investigating the role of educational robotics in formal mathematics education"
<p>This repository contains the material required to reproduce the study looking to investigate the role of educational robotics in formal mathematics education for 15 year old students in the French speaking region of Switzerland. This includes :</p> <ul> <li> <p>Pedagogical content in the form of both teacher and student resources</p> </li> <li> <p>Data collection ressources (surveys and tests)</p> </li> </ul> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.4649842</p> <p>• The corresponding article : Brender, J., El-Hamamsy, L., Bruno, B., Chessel-Lazzarotto, F., Zufferey, J.D., Mondada, F. (2021). Investigating the Role of Educational Robotics in Formal Mathematics Education: The Case of Geometry for 15-Year-Old Students. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds) Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science(), vol 12884. Springer, Cham. https://doi.org/10.1007/978-3-030-86436-1_6</p> <p>• Licence : CC-BY</p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Robotic Monitoring of Alpine Screes: a Dataset from the EU Natura2000 habitat 8110 in the Italian Alps
<p>Data collected between the 19th and the 21rd of July 2022, in Valfurva, 23030 (SO), Italy, within the Stelvio National Park, located inside the Natura 2000 SPA IT2040044. 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> </p> <p>The dataset contains two different sets of data:</p> <p>1) typical and early warning species data - videos of seven different typical species of the habitat 8110 and one early warning species.</p> <p>2) monitoring mission data - video of the monitoring mission, robot status and point clouds, pictures and videos taken by the robot during the autonomous surveys.</p> <p> </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>
Dynamic robotic tracking of underwater targets using reinforcement learning
To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new approaches and techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study is to investigate the use of deep reinforcement learning as a tool for range-only underwater target tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.
Three-dimensional CAD model of the robotic system used for acquiring samples from bacterial swarms
<p>This CAD model shows the robotic sampling system that was used in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of <em>Bacillus subtilis</em> swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution. </p> <p>The CAD model consists of 81 individual files in IPT or IAM format, which need to be loaded together into a AutoDesk Inventor to be viewed. We used AutoDesk Inventor 2021 to create and view this CAD model. </p>
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>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</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>
Eye Tracking in Robot Control Tasks
<p>This table is part of a systematic review. It contains current work of researchers around the world, who work in the field of eye tracking control for robotic arms. These controls are used for assistive robotics, aiding physically impaired people in everyday life and in shared workspaces.</p><p>This data set can also be found on git: https://github.com/AnkeLinus/EyeTrackingInRobotControlTasks.git </p><p>If you use this table in other publications please cite as stated in the git repository. Also keep an eye open for updates.</p>
Robotic Versus Electromagnetic Bronchoscopy for Pulmonary LesIon AssessmeNT: (the RELIANT Trial)
ClinicalTrials.gov study NCT05705544. IPD Sharing: YES. Countries: 1. Publications: 12.
The Effect of Deep Versus Moderate Muscle Relaxants in Men During and After Robotic Surgery for Prostate Cancer
ClinicalTrials.gov study NCT03808077. IPD Sharing: YES. Countries: 1. Publications: 1.
Simulation, robot codes and figure data from collective phototactic robotectonics
Open the record for dataset details and reuse information.
Biomimetic robotic skin implemented with hydrogel-elastomer hybrids and tomographic imaging methods
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Data from: Stretchable Arduinos embedded in soft robots
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Robotic manipulation datasets for offline compositional reinforcement learning
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Dynamic robotic tracking of underwater targets using reinforcement learning
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Data and trained models for: Human-robot facial co-expression
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.