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13 results for “robotic behaviors”
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <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> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: 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><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: 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> <p>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.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <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> <p>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.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>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:</p> <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> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"
<p>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), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897</p> <p>Contents: </p> <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> <p>See the readme and summary files contained within the archives for further details.</p>
Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"
<pre># Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"<br>Author: Naoto Yoshida<br><br>How to use:<br>1. Clone https://github.com/ugo-nama-kun/journalpaper_robot_2024 from github.<br>2. Extract data_20241119.zip in the cloned repository.<br>3. Run each plot_Fig*.py</pre>
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations
<h3><strong>Dataset Description</strong>:</h3> <p>This dataset contains 99,522 episodes of RGB-based state-action-reward expert demonstrations collected from a reaching task within a surgical robotics simulation environment, LapGym (Scheikl et al.). The data was generated using the LapGym ReachEnv, where a robotic grasper is tasked with reaching a specific point in 3D space. Each episode consists of a series of RGB images (64x64 pixels), corresponding actions, rewards, and terminal flags, designed for training behavior cloning and offline RL algorithms.</p> <p>This dataset was created for the paper "Assessing Behavior Cloning with RGB Inputs in Surgical Robotics Through Dataset Ablation". The expert demonstrations were collected using an optimal agent, where actions were computed based on the known locations of the grasper and the point of interest.</p> <p>The specific settings for the ReachEnv environment used to collect the dataset are as follows:</p> <ul> <li><strong>Environment</strong>: <code>ReachEnv</code></li> <li><strong>Observation Type</strong>: <code>RGB</code></li> <li><strong>Render Mode</strong>: <code>HUMAN</code></li> <li><strong>Action Type</strong>: <code>CONTINUOUS</code></li> <li><strong>Distance to Target Threshold</strong>: <code>0.01</code></li> <li><strong>Image Shape</strong>: <code>(64, 64)</code></li> <li><strong>Frame Skip</strong>: <code>1</code></li> <li><strong>Time Step</strong>: <code>0.1</code></li> <li><strong>Reward Amounts</strong>: <ul> <li><strong>Distance to Target</strong>: <code>0.0</code></li> <li><strong>Delta Distance to Target</strong>: <code>0.0</code></li> <li><strong>Successful Task</strong>: <code>100.0</code></li> <li><strong>Time Step Cost</strong>: <code>0.0</code></li> <li><strong>Workspace Violation</strong>: <code>0.0</code></li> </ul> </li> <li><strong>Sphere Radius</strong>: <code>0.020</code></li> </ul> <p>Key features of the dataset include:</p> <ul> <li><strong>RGB Inputs</strong>: Each episode includes 64x64 RGB frames representing the environment's visual state.</li> <li><strong>Optimal Demonstrations</strong>: All actions represent optimal behavior for completing the reach task.</li> <li><strong>Sparse Rewards</strong>: Rewards are only provided upon successful task completion, offering a challenging learning scenario.</li> <li><strong>Varied Episode Lengths</strong>: Episodes vary in length, depending on how quickly the task is completed.</li> </ul> <h3><strong>Applications</strong>:</h3> <p>This dataset is designed for research in:</p> <ul> <li>Behavior cloning with RGB image inputs.</li> <li>Data efficiency and sample efficiency in imitation learning.</li> <li>Offline reinforcement learning with visual inputs.</li> </ul> <h3><strong>Structure</strong>:</h3> <ul> <li><strong>Observations</strong>: Images stored as 64x64 RGB pixel arrays.</li> <li><strong>Actions</strong>: Continuous actions corresponding to the robotic grasper’s movements.</li> <li><strong>Rewards</strong>: Sparse rewards indicating task success.</li> <li><strong>Terminals</strong>: Terminal flags for task completion.</li> </ul> <h3><strong>How to Use</strong>:</h3> <p>This dataset can be used to train and evaluate offline models for robotic control tasks in conjunction with LapGym, particularly in the domain of surgical robotics. It is especially suited for behavior cloning experiments, offline reinforcement learning, and studies on data efficiency.</p> <h3><strong>Citation</strong>:</h3> <p>Please cite this dataset in any publications as:<br><em>Acs and Zhong (2024). RGB-Based Behavior Cloning Dataset for Surgical Robotics: 99,522 Episodes of Optimal Demonstrations. </em></p>
Raw onboard logs for "Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots"
<p>This raw data archive includes the onboard logs from the swarm of aquatic robots used to produce the paper Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots by M. Duarte et al. (2016).</p> <p>See readme.txt for more details.</p>
Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot
<p>This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors.</p> <p>The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.</p>
Data from: Recent biological invasion shapes species recognition and aggressive behavior in a native species: a behavioral experiment using robots in the field
Open the record for dataset details and reuse information.
Asymmetries-induced nonlinear dynamic behaviors enable a versatile modulation strategy for insect-scale robotics
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Data from: Hidden Markov models reveal tactical adjustment of temporally-clustered courtship displays in response to the behaviors of a robotic female
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Behav'Robot: Understanding the Surgeon Behavior During Robot-assisted Surgery
ClinicalTrials.gov study NCT04869995. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparing Imitation and Stereotyped Behaviors in Autistic Children: Robots vs. Human Operators
ClinicalTrials.gov study NCT06144528. IPD Sharing: NO. Countries: 1. Publications: 2.
Live programming controlled experiment on state machines for robotic behaviors
<p>All the information about the controlled experiment performed on the Live Robot Programming language (LRP) vs SMACH (Python API), both for program comprehension and program writing</p> <ul> <li>Programs for both SMACH and LRP</li> <li>Introductory material for both SMACH and LRP</li> <li>Questionnaires</li> <li>Raw and processed data</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.