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ShareScore release 0.9.0
Dataset results
12 results for “robotic perception”
Perception Sensor Dataset For Bioinspired Landing Trajectories Of An Ornithopter Robot
<p>The dataset contains the measurements captured by several onboard sensors during the landing maneuvers of an ornithopter robot. Each dataset contains a ROS bag file with the sensor measurements, a file with the bioinspired trajectory, a file with the events generated by the simulated event-based sensor, and a README file with the instructions to use the dataset.</p> <p>The bioinspired landing trajectories are computed using Tau Theory. Each landing trajectory test was performed in a simulated scenario. The object models of each scene can be found in the /model/meshes folder of each scene. There are two testing scenes: (i) a warehouse and (ii) a refinery. The file object_pose.csv includes the position and orientation of each object in the scene. The sensor measurements were saved in rosbag file that contains a topic for each sensor measurement. The dataset includes information from the following simulated sensors:</p> <ul> <li>Velodyne HDL-32E</li> <li>Sonar sensor with a range of 20 m</li> <li>IMU</li> <li>Frame based monocular camera</li> <li>Event camera</li> </ul>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)
<p>Dataset with evaluation results of the paper "New Metrics for Industrial Depth Sensors Evaluation for Precise Robotic Applications", DOI <a href="https://doi.org/10.1109/IROS51168.2021.9636322">10.1109/IROS51168.2021.9636322</a></p>
Background-Foreground-Segmentation Labels for "Self-improving Semantic Perception on a Construction Robot"
<p>Background-foreground-segmentation labels the paper "Self-improving Semantic Perception on a Construction Robot" of CoRL 2021</p>
Dataset AEROARMS - Perception for robot operation
<p><strong>Dataset description: </strong>This dataset contains the images used for validating the perception algorithms to support aerial and ground robot operations.</p> <p><strong>Data description:</strong> The images where obtained using an Intel Realsense d435 camera. Additionally, the calibration of the camera is given in XML format.</p> <p><strong>Dataset size: </strong>72.8Mb</p>
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>
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
<p>This dataset is a part of the supplementary materials to the 2017 RAL <a href="https://ieeexplore.ieee.org/document/7358076">article</a> with the same title.</p> <blockquote> <p>A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots</p> <p>IEEE Robotics and Automation Letters</p> <p>Alessandro Giusti, Jerome Guzzi, Dan Ciresan, Fang Lin He, Juan Pablo Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jurgen Schmidhuber, Gianni A. Di Caro, Davide Scaramuzza, Luca Gambardella</p> </blockquote> <p>You can find more information on the <a href="http://bit.ly/perceivingtrails">project web page</a> (alessandrog@idsia.ch).</p> <p><strong>Dataset</strong></p> <p>Folders 001..010 contain the dataset used to train the networks. Folder 000 contains preliminary test data. Folders 011..014 contain data for testing the system.</p> <ul> <li>000 and 003 were shot with an handheld cellphone.</li> <li>001 and 002 were shot with 3 GOPRO Hero 3 cameras, fixed on the head with straps.</li> <li>004..014 were shot with 3 Bluefox cameras, fixed on a rigid helm (the same model and with the same lens as the camera mounted on the quadcopter).</li> </ul>
Public Perceptions Toward Robotic Surgery, Telesurgery and Telemedicine
ClinicalTrials.gov study NCT06803719. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Open source code for Brain-inspired bodily self-perception model for robot rubber hand illusion
<p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi">RHI</a></p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_matlab">RHI_Matlab</a></p> <p>This file is the open-source code for 'Brain-inspired bodily self-perception model for robot rubber hand illusion', mainly used in simulation environments, and can reproduce various rubber hand illusion experiments.</p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_braincog">RHI_BrainCog</a></p> <p>We are building an open source spiking neural network based brain-inspired cognitive intelligence engine for Brain-inspired Artificial Intelligence and brain simulation. Therefore, we also implemented the core Proprioceptive drift experiment of the rubber hand illusion experiment using Braincog.</p> <p><a href="https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Embodied_Cognition/RHI">The open source code built by BrainCog</a></p> <p>BrainCog provides essential and fundamental components to model biological and artificial intelligence. The current version of BrainCog contains at least 18 functional spiking neural network algorithms (including but not limited to perception and learning, decision making, knowledge representation and reasoning, motor control, social cognition, etc.) built based on BrainCog infrastructures, and BrainCog also provide brain simulations to drosophila, rodent, monkey, and human brains at multiple scales based on spiking neural networks at multiple scales.</p>
Hospitality Oriented Service Perception Evolution With Robots (HOSPER)
ClinicalTrials.gov study NCT07002411. IPD Sharing: NO. Countries: 1. Publications: 0.
PEPPER : PErcePtion of Pre-anesthesia Visit With or Without Robot
ClinicalTrials.gov study NCT03521453. IPD Sharing: NO. Countries: 1. Publications: 0.
ROS2 bag multimodal perception dataset acquired in Portuguese forest with Modular-E robot for line vegetation clearing
<p>Rosbag data acquired:</p> <ul> <li>RGB-D data from OAK-D facing sideways;</li> <li>Pointcloud from Hokuyo 2D Lidar facing sideways;</li> <li>Pointcloud from Robosense 360º Lidar facing forward;</li> <li>RTK fix.</li> </ul>
ScienceDex guides
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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.