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2,639 results for “Robotic”
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 27
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2019 Week 28
<p>These data were collected from robot controller during solar tanks welding operation.</p>
Benchmark EEG data set for trust assessment for interactions with social robots
<p>The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation.</p> <p>The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time.</p> <p>The EEG data provides insights into the electrical activity of the brain, offering a window into cognitive processes and emotional responses during various activities or stimuli in the form of microvolt and with a frame rate of 128 Hz. The whole data set consists of 3651124 data points for each sensor, i.e. 173863 on average for each participant (min. 128505, max. 249631). </p> <p>Files are named after participant numbers starting with ID01. The data has to be pre-processed making use of the information given in the details.xlsx file that contains annotations corresponding to the EEG recordings. These annotations denote the timing of different phases related to trust across the participants' interactions. Each phase is delineated by a start time and an end time, representing distinct stages of the trust-building process. All the other data (timestamps) which are outside the start and end of each phase should be considered as breaks, e.g. filling out the questionnaires. The last element is the trust score for the given phase, which is calculated on the answers in an MDMT questionnaire.</p> <p>The following phases have been annotated:</p> <ol> <li>Trust Building: This phase involves friendly initial interactions for establishing trust between participants and the robot.</li> <li>Situational Awareness: This phase continues to build up trust by showing situation awareness of the robot, e.g. by complimenting on the participant's fashion choice.</li> <li>Transparency: Trust is maintained by increased openness and clarity in communicating about the robot's abilities.</li> <li>Trust Violation: Trust is compromised during this phase by deliberately misleading the participant and making it impossible to answer correctly. </li> <li>Trust Repair: The robot shows efforts to repair trust by apologizing for the behavior in the previous stage.</li> </ol> <p>If you work with the data, please cite one of the article given below.</p>
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>
Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework
<h1><strong><span>Data Description and File Structure:</span></strong></h1> <p>This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories.</p> <p>Three main folders are found in this repository:</p> <ul> <li><code><strong>1FA-single-factor-auth/</strong></code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code><strong>2FBC_two-factor-blockchain/</strong></code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code><strong>BB_blockchain-base/</strong></code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, <u>doi/10.1126/scirobotics.abm4636</u></li> </ul> </li> </ul> <p>Under each directory, we have the following folders:</p> <ul> <li><code><strong>exp_1/</strong></code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code><strong>exp_2/</strong></code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code><strong>exp_3a/</strong></code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code><strong>exp_3b/</strong></code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code><strong>exp_4/</strong></code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> <p>Each experiment configuration is carried out in 20 repetitions.</p> <h3><strong><em>Experiment 1 (exp_1):</em></strong></h3> <ul> <li><code><strong>8rob-2byz/</strong></code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code><strong>16rob-4byz/</strong></code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><strong><code>24rob-6byz/</code> </strong>Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3><strong><em>Experiment 2 (exp_2):</em></strong></h3> <ul> <li><code><strong>24rob-5floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code><strong>24rob-25floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><strong><code>24rob-45floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><strong><code>24rob-75floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3><strong><em>Experiment 3a (exp_3a):</em></strong></h3> <ul> <li><strong><code>24rob-0byz/</code> </strong>Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><strong><code>24rob-3byz/</code> </strong>Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code><strong>24rob-6byz/</strong></code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><strong><code>24rob-9byz/</code> </strong>Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3><strong><em>Experiment 3b (exp_3b):</em></strong></h3> <ul> <li><strong><code>24rob-6byz-1style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><strong><code>24rob-6byz-2style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><strong><code>24rob-6byz-3style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code><strong>24rob-6byz-4style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code><strong>24rob-6byz-5style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><strong><code>24rob-6byz-6style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3><strong><em>Experiment 4 (exp_4):</em></strong></h3> <ul> <li><strong><code>8rob-2byz/</code> </strong>Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><strong><code>16rob-4byz/</code> </strong>Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code><strong>24rob-6byz/</strong></code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><strong><code>72rob-18byz/</code> </strong>Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><strong><code>96rob-24byz/</code> </strong>Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><strong><code>120rob-30byz/</code> </strong>Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3><strong>Relevant Files:</strong></h3> <ul> <li><code><strong>block.csv</strong></code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code><strong>estimate.csv</strong></code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code><strong>sc.csv</strong></code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><strong><code>extra.csv</code></strong> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3><strong>Relevant Data Fields:</strong></h3> <ul> <li><code><strong>ID</strong></code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code><strong>TIME</strong></code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code><strong>TELAPSED</strong></code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code><strong>TIMESTAMP </strong></code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code><strong>BLOCK </strong></code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code><strong>SIZE</strong></code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><strong><code>ESTIMATE</code> </strong>The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><strong><code>MEAN</code> </strong>The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code><strong>VOTECOUNT</strong></code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code><strong>VOTEOKCOUNT</strong></code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><strong><code>C?</code> </strong> A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code><strong>CPU</strong></code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code><strong>RAM</strong></code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code><strong>KB</strong></code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>
Spatial Room Impulse Response Dataset: A Robot's Journey Through Coupled Rooms of a Reverberant University Building
<p>This is a dataset of Spatial Room Impulse Responses obtained by a robot equipped with a microphone array.</p> <p>The measurements were conducted in a reverberant university building, the <em>Helmholtz</em> building at<em> Technische Universität Ilmenau</em> (coordinates: N50.6815788133375°, E10.939294371903342°). All the floors in the building are covered with bare stone tiles, the walls are not acoustically treated. Only the hallway has a suspended acoustic ceiling. The file "Pictures Overview.jpg" shows some impressions of the building. Note that the floorplan only shows parts of the building that were connected to the measurement area by open doors.</p> <p>The area covered by the robot is in a hallway on the top floor (2nd floor starting with ground floor) with two stairwells at both ends. To specifically study the behavior of coupled rooms and occluded sources, the sound sources were placed in adjacent sections of the building and on multiple floors. See the file "Measurement Overview.jpg" for an overview of the source positions and the receiver areas covered. Areas 2 and 3 were captured with a higher spatial resolution than area 1 to analyze the transition between the hallway and the staircases. The receiver positions form a uniform grid, the pitch between positions is shown in the following table. Due to time and technical constraints, only a maximum of 3 sources were used per run, so there are not all combinations of sources and receiver areas. Refer to the following table to see which source was active for which area and which zip file contains the according data:</p> <table> <tbody> <tr> <th>Filename</th> <th>Sources</th> <th>Receiver Area</th> <th>Receiver Positions [ct]</th> <th>Pitch [cm]</th> </tr> </tbody> <tbody> <tr> <td>Helmholtzbau_OG2_HM_HS.zip</td> <td>HM, HS</td> <td>Area 1</td> <td>143</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SML_SSL_SSU.zip</td> <td>SML, SSL, SSU</td> <td>Area 1</td> <td>154</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SMU_SML_HM.zip</td> <td>SMU, SML, HM</td> <td>Area 2</td> <td>88</td> <td>25</td> </tr> <tr> <td>Helmholtzbau_OG2_SSU_SSL_HS.zip</td> <td>SSU, SSL, HS</td> <td>Area 3</td> <td>92</td> <td>25</td> </tr> </tbody> </table> <p>As an example "Plot Reverberation Time.jpg" shows the reverberation times for all measured positions of Area 1 and 2 with speaker HM.</p>
Live robot dance at the Orebro Feast Concert
<p>A human pianist, a virtual drummer and a dancing robot, all coordinated by AI. This unusual team entertained the audience of the "Festkonsert", an official yearly celebration of Örebro University with the participation of about 250 people. The performance was meant to show the use of AI to orchestrate the collaboration among human and artificial agents in a creative process. The music performed by the pianist was improvised, and an AI system decided the moods, patterns and motions to be performed by the virtual drummer and by the dancing robot.</p> <p>This clip is a recording of the public event.</p> <p><strong>Artists:</strong> Peter Knudsen (human pianist), Pepper (the robot), Oscar Thörn (AI programmer), Alessandro Saffiotti (AI expert)</p>
Designed and generated Robot and Furniture datasets
<p>This dataset is a public data set designed to promote the application of artificial intelligence technology in reverse engineering. The dataset contains mesh objects with different parameters that are automatically generated from the manually designed CAD model database, and the labels are the specific parameter sizes corresponding to these objects. Researchers can read these objects and generate different variants for different tasks.</p>
ARIVVD: Aberystwyth Robot Infant Vision Video Dataset
<p>This is the Aberystwyth Robot Infant Vision Video Dataset, created for developmental robotics research at Aberystwyth University. This dataset was created for an internally funded research pilot project (“Babyvision: a robotic investigation into early development of colour constancy”). </p>
ARL Human-Robot Dialogue (HuRDL) Corpus
<p>ARL HuRDL (Human Robot Dialogue Learning) is a labeled corpus of 22 human-human dialogues in a situated reference resolution task. </p>
Dataset for Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation
<p>This data set was created between 01-04.2021, to study the current landscape of using web trackers in scholarly communication. The data set is part of an article (manuscript) that is intended to be published under the this title: Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation.</p>
A Physical Human-Robot Interaction Dataset - TacAct
<p>This dataset is the supplementary material of the IROS 2021 article "Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions".<br> The dataset was collected by a flexible supercapacitor tactile sensor installed in an imitated mechanical arm device. In a 32 × 32 grid, the sensor data can be sampled at 100 Hz (100 frames per second). A total of 12 touch actions, namely, pull, squeeze, push, hold, grasp, poke, static drag, strong hit, soft slide, scratch, soft tap, and sliding drag, were recorded. A single-action collected from a subject consists of a 32×32×N matrix (where N is the number of frames or frame length). For the same action, all subjects were asked to use as many postures as possible to apply different forces to different positions of the sensor, and repeat 20 times with each hand. Except for strongly hit and soft tap (complete in an instant, acquisition time 2 s), the duration of each action is 2 s, and the time is 4 s altogether. The experiment was conducted on 50 subjects (36 males and 14 females,ranged from 22 to 36 years and 44 were right-handed) in total, each subject consists of 480 actions (12 actions × 40 repetitions ) in total, and the dataset collected 24,000 actions from the subjects.</p>
Robot@VirtualHome dataset
<p>The Robot@VirtualHome dataset is a raw collection of data from 30 virtual homes with different appearance obtained through the Robot@VirtualHome ecosystem. Each virtual house imitates a real house, keeping the same room layout and imitating real objects with virtual object models. The objectives of this dataset are: first, to be used as a testbed for diverse algorithms such as semantic mapping through the categorization of objects and/or rooms, active exploration of the environment, localization by appearance, or others where the data presented are of interest, and second to provide a basic example of the results that can be obtained through the Robot@VirtualHome ecosystem.</p> <p>The dataset consists of 113278 captures in 30 houses, with 236 rooms, 2569 objects and 4 different appearance conditions. Each data capture has stored an RGB image, a depth image, a semantic mask image, the measurements from a laser scanner and a log with information about the position at which the data was taken. In addition, for each house we have added the occupancy map obtained with the laser scanner and a log with the ground truth of all objects and rooms.</p> <p>Five raids have been carried out for each house: the first one, capturing data at the nodes of a grid using standard appearance, in the remaining four raids the data were taken by wandering around visiting all the rooms and using different appearance conditions.</p> <p>More detailed information is provided in the article.</p> <p>An API is available <a href="https://github.com/DavidFernandezChaves/RobotAtVirtualHome-Dataset-API">here </a>to facilitate access to the dataset data.</p>
Vector image of processing techniques for self-healing soft robots
<p>Vector images of processing techniques that can be used to manufacture self-healing soft robots.</p> <p>The file includes different types of additive manufacturing processes (fused filament fabrication, direct ink writing, selective laser sintering, stereolithography, inkjet printing, fused granulate fabrication), formative processes (compression moulding, solvent casting, injection moulding, casting, vacuum assisted resin transfer moulding, blow moulding), and assembly processes (folding & binding, joining & binding, stacking and binding, local thermal ablation & welding).</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>
RoboFinch: a versatile audio-visual synchronized robotic bird model for laboratory and field research on songbirds
<p>Raw data and R script for the behavioral data published in the Journal Methods in Ecology and Evolution with the title: RoboFinch: a versatile audio-visual synchronized robotic bird model for laboratory and field research on songbirds</p> <p>All data and source files for the RoboFinch construction can be found here: https://doi.org/10.5281/zenodo.7520589</p> <p> </p>
Data for: Adapting small jumping robots to compliant environments
<p>Jumping animals launch themselves from surfaces that vary widely in compliance from grasses and shrubs to tree branches. However, studies of robotic jumpers have been largely limited to those jumping from rigid substrates. In this paper, we leverage recent work describing how latches in jumping systems can mediate the transition from stored potential energy to kinetic energy. By including a description of the latch in our system model of both the jumper and compliant substrate, we can describe conditions in which a jumper can either lose energy to the substrate or recover energy from the substrate resulting in an improved jump performance. Using our mathematical model, we illustrate how the latch plays a role in the ability of a system to adapt its jump performance to a wide range of substrates that vary in their compliance. Our modeling results are validated using a 4 g jumper with a range of latch designs jumping from substrates with varying mass and compliance. Finally, we demonstrate the jumper recovering energy from a tree branch during take-off, extending these mechanistic findings to robots interacting with a more natural environment.</p>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
A robotic honeycomb for interaction with a honeybee colony
<p>This repository contains data for the results described in:</p> <p> Barmak R. and Stefanec M., Hofstadler D., Piotet L., Schnwetter-F-S S., Mondada F., Schmickl T., Mills R.,<br> A robotic honeycomb for interaction with a honeybee colony.<br> Science Robotics, 2023<br> DOI: 10.1126/scirobotics.add7385</p> <p> </p>
High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics
<p>Data to support the paper - <em>High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics</em>. Data includes a summary of X-ray and LCMS results for the reactions executed on the OpenTrons, output reports and summaries from MSCheck (semi-automated LCMS analyzer tool) and the Python scripts used to execute single and multistep chemistry on the OpenTrons.</p> <p><strong>Abstract</strong></p> <p>We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way to collect structural data on a binding site. Starting from the high information content of the crystallographic fragment screens on PHIP(2) (second bromodomain of the pleckstrin homology domain interacting protein), a collection of more than 1800 compounds was enumerated. Several thousand <em>Crude Reaction Mixtures</em> (CRMs) were synthesized on one robotic platform, an OpenTrons OT-1 liquid handler, using reaction sequences of up to 5 chemical steps. Analysis via MScheck, an algorithm-based system for finding a m/z in a CRM, significantly shortened product identification protocol times. 957 usable X-ray diffraction datasets were acquired, which resolved as 22 reaction products binding to the protein, 19 with conserved poses relative to the original fragment and 3 with a new, unexpected binding pose. The 22 crystallographic hit compounds were subsequently tested with peptide displacement alpha-screen assay and time-resolved grating-coupled interferometry-based biosensor assays, which confirmed one molecule with an IC<sub>50</sub> = 34 μM and K<sub>D</sub> = 50 μM, from an inactive fragment. The procedures described are entirely formulaic and engineerable and the method is eminently scalable. We anticipate that this cheap, low solvent-use approach will yield vast amounts of data, enabling rapid SAR landscape exploration around fragments, leading to faster fragment to lead times.</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.