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87 results for “Learning Environment”
Learning to Grasp Unknown Objects in Domestic Environments with GP-net+
<p>This record includes data for the paper "Learning to Grasp Unknown Objects in Domestic Environments", currently under review.<br><br><strong>Simulation environment with pre-trained GP-net+ model</strong><br><br>The paper presents a simulation environment for grasping objects in domestic environments. The presented objects and furniture units, as well as a pre-trained GP-net+ model can be found in the "gpnetplus_simulation_data.zip" file. After this zip file is downloaded, it can be unpacked it into the <a href="https://github.com/AuCoRoboticsMU/GP-netplus" target="_blank" rel="noopener">GP-net+ directory</a>. It includes all necessary data to use the simulation environment, for example, for testing GP-net+ or other grasping models in simulated domestic environments.</p> <p> </p> <p><strong>ROS model</strong></p> <p>The paper additionally presents an <a href="https://github.com/AuCoRoboticsMU/GP-netplus-ros" target="_blank" rel="noopener">ROS package</a> that can be deployed for grasping unknown objects in domestic environments with simulated or real robots. We make a ROS-compatbile model of GP-net+ available in the "ros_gpnet_plus.zip" file, which can be used with the ROS package.</p> <p> </p> <p><strong>Training dataset</strong></p> <p>We used the simulation environment in our paper to generate a training dataset and train GP-net+. This training dataset is included in this record and can be used to replicate our results or train modifications of GP-net+.</p> <p>To improve handling of the training dataset (total size 25GB+), we split the dataset into several .zip files, named val.zip (validation data) and train_[0-6].zip (training data). Download all files individually and extract them into a single folder. Combine all files train_[0-6].zip directory into a single directory called 'train', for example, by using the 'move_train_data.sh' script provided.<br><br>The final structure for the dataset should look similar to this:<br><br>gpnet_data</p> <p>|-- val</p> <p> |-- depth_image_0000000.npz</p> <p> |-- depth_image_0000001.npz</p> <p> ...</p> <p> |--segmask_image_0052346.npz</p> <p>|-- train</p> <p> |-- depth_image_0000000.npz</p> <p> |-- depth_image_0000001.npz</p> <p> ...</p> <p> |-- segmask_image_0602506.npz</p> <p> |-- segmask_image_0602507.npz</p> <p><br><br><br>For generation of the training and simulation data, the following mesh databases have been used:<br><br>B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar,"Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set," IEEE Robotics and Automation Magazine, vol. 22, no. 3, pp. 36–52, 2015<br><br>A. Singh, J. Sha, K. S. Narayan, T. Achim, and P. Abbeel, "BigBIRD: A large-scale 3D database of object instances," 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 509–516, 2014.<br><br>A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, "ShapeNet: An Information-Rich 3D Model Repository," Tech. Rep. arXiv:1512.03012 [cs.GR], Stanford University — Princeton University — Toyota Technological Institute at Chicago, 2015.</p> <p>D. Morrison, P. Corke, and J. Leitner, "EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation," IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4368–4375, 2020</p>
Current Challenges In School Administration: Developing Strategies For A Resilient Learning Environment
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MultiTune: Multiple-Environment Configuration Tuning via Multi-Task Learning and Propensity Score Matching
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Data for: Communicating with deaf patients in the clinical environment: Lessons learned from a virtual patient panel
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Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors
<p>Supplementary Information: Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors</p>
Prediction of olivine in distinct forming-environments using machine learning and implications for magmatic sulfide prospectivity
<p>Olivine compositions from global volcanic and plutonic samples.</p>
Companion dataset for paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review"
<p>This dataset is a companion of extra materials for the paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review" which focuses on a literature review about pedagogical uses, practices and strategies with Immersive Learning Environments (ILE) that have an explicit focus on Self-regulation of Learning (SRL). This dataset is an file containing information about: (1) list of papers forming the corpus of the literature review; (2) Criteria for associating accounts of SRL with ILE in relation to the Beck et al. (2020) Framework; and (3) Graphs with Overviews of studies about ILE for SRL. This dataset is hosted on zenodo, a data repository.</p>
Profiles of Social and Emotional Learning Skills and Learning Environment Factors in Adolescents: A Latent Profile Analysis
<p>data</p>
LEARN: Learning Environment for Artificial Intelligence in Radiotherapy New Technology
ClinicalTrials.gov study NCT05184790. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Catecholaminergic regulation of learning rate in a dynamic environment
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Data from: Age and early social environment influence guppy social learning propensities
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Data from: Social learning in a high-risk environment: incomplete disregard for the ‘minnow that cried pike’ results in culturally transmitted neophobia
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Evaluation of an Interactive E-learning Environment to Enhance Digital Health Literacy in Cancer Patients
ClinicalTrials.gov study NCT07200453. IPD Sharing: YES. Countries: 1. Publications: 0.
Machine Learning Approach to Study the Interactions Between Environment and Intestinal Tissue Homeostasis in IBD
ClinicalTrials.gov study NCT06120322. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
MOVI-OLE! [Open Learning Environments]
ClinicalTrials.gov study NCT07103343. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effectiveness of Parent-child Collaborative Game on Children Attentiveness in Informal Learning Environment
ClinicalTrials.gov study NCT06966895. IPD Sharing: NO. Countries: 1. Publications: 0.
Rendering of a Local 1g Environment for Enhanced Motor Learning in Altered Gravity
ClinicalTrials.gov study NCT03978910. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of the Effects of Virtual Reality Learning Environment on Nursing Student Non-technical Skills Development
ClinicalTrials.gov study NCT06277557. IPD Sharing: NO. Countries: 1. Publications: 0.
Motor Learning of Stroke Patients in Virtual Environments
ClinicalTrials.gov study NCT03583827. IPD Sharing: NO. Countries: 1. Publications: 0.
Motivation, Learning and Decision Making in Changing Environments
ClinicalTrials.gov study NCT07314112. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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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
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