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2,639 results for “Robotic”
Data-driven calibration of RAVEN-II surgical robot with ground truth joint positions
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Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
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Data and code from: Head-mounted surgical robots are an enabling technology for subretinal injections
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Plasticized electrohydraulic robot autopilots in the deep sea
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Data from: Salmon behavioural response to robots in an aquaculture sea cage
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A laser-microfabricated electrohydrodynamic thruster forcentimeter-scale aerial robots
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Development of compositionality through interactive learning of language and action of robots
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Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy
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PIV results from a kinematic study of robotic bird wings
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A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking
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Tetanus-driven biohybrid multi-joint robots powered by muscle rings with enhanced contractile force
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Dataset from: In-situ bidirectional human-robot value alignment
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Video Editing Materials for Human Perceptions of a Curious Robot that Performs Off-Task Actions
<p>Video footage, editing timelines and compositing resources for the user study described in the HRI 2020 paper "Human Perceptions of a Curious Robot that Performs Off-Task Actions." Adobe Premiere 14 (CC 2019) or greater and a matched release of Adobe After Effects are required to render the clips.</p>
Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach"
<p>Supplementary video of the paper "Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach" showing the experimental results.</p>
Robot action execution model learning data
<p><strong>Short summary</strong></p> <p>This dataset accompanies our paper</p> <p><code>A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Representation and Experience-Based Learning of Explainable Models for Robot Action Execution," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.</code></p> <p> </p> <p><strong>Contents</strong></p> <p>There are three zip archives included, each of them a dump of a MongoDB database corresponding to one of the three experiments in the paper:</p> <ul> <li>Grasping a drawer handle (<em>handle_drawer_logs.zip</em>)</li> <li>Grasping a fridge handle (<em>handle_fridge_logs.zip</em>)</li> <li>Pulling an object (<em>pull_logs.zip</em>)</li> </ul> <p>All three experiments were performed with a Toyota HSR. Only the data necessary for learning the models used in our experiments are included here.</p> <p> </p> <p><strong>Usage</strong></p> <p>After unzipping the archives, each database can be restored with the command</p> <blockquote> <p>mongorestore [directory_name]</p> </blockquote> <p>This will create a MongoDB database with the name of the directory (<em>handle_drawer_logs</em>, <em>handle_fridge_logs</em>, and <em>pull_logs</em>).</p> <p>Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository</a>.</p>
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics
<p><strong>Objectives</strong></p> <p>We introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.<br> Read <strong>00-README.txt</strong> for detailed download instructions.</p> <p>More details on the dataset at <a href="https://github.com/vhavard/InHARD">https://github.com/vhavard/InHARD</a></p> <p>This work has been performed at the CESI LINEACT : <a href="https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/">https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/</a></p>
Dataset for systematic mapping literature about STEAM through Challenge-Based Learning, Robotics and Physical Devices
<p>This is the public dataset for the systematic mapping literature review performed for the paper "Fostering STEAM through Challenge-Based Learning, Robotics and Physical Devices: A systematic mapping literature review".</p> <p>Related to activity O2.A1 of the RoboSTEAM European Project.</p>
Replication package of the paper titled "Mining the ROS ecosystem for Green Architectural Tactics in Robotics and an Empirical Evaluation".
<p>This is the replication package of the paper titled "Mining the ROS ecosystem for Green Architectural Tactics in Robotics and an Empirical Evaluation".</p> <p>The replication package is structured according to the research questions of the study (RQ1 and RQ2) and it is composed of the following elements:</p> <ul> <li><strong>Supplementary Material (RQ1).pdf</strong>: a 93-pages technical report providing the details about our mining activities and application of thematic analysis for identifying green architectural tactics for robotics software.</li> <li><strong>Supplementary Material (RQ2).pdf</strong>: a 75-pages technical report detailing the design, conduction, and results of the empirical assessment of the identified green tactics.</li> <li><strong>RQ1_data_software.zip</strong>: the raw data and mining source code related to all the activities we carried out for answering RQ1.</li> <li><strong>RQ2_data.zip</strong>: the raw data related to all the activities we carried out for answering RQ2.</li> <li><strong>RQ2_ros_implementation.zip</strong>: the source code of the ROS-based system we implemented for performing the experiment in RQ2.</li> </ul>
Data from: Evolutionary online behaviour learning and adaptation in real robots
Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
Software for control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms
<p>This software implements the autonomous control of a robot by using a fuzzy logic controller tuned by a genetic algorithm. The software was written in C programming language for Windows (SDK). A description of the software can be found in the research publication "Arsene, C.T.C., & Zalzala, A.M.S., "Control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms", In Proc Congress on Evolutionary Computation, Vol. 1, pp. 428-35, Washington DC, 1999, IEEE Computer Science Press, ISBN 0-7803-5536-9". Possibly the software to be used also for simulation of Nano-robots.</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.