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7 results for “Object manipulation”
Data of "Pen mates' interactions, potential precursors of damaging behaviours, object manipulation, straw rooting, and primary activity: A detailed data set in undocked pigs under dietary protein restriction"
<p>Damaging behaviours, such as tail biting, are common problems in pig production, compromising animal welfare and causing economic losses. Detailed studies are impeded by the difficulty of directly observing these behaviours. Tail biting is a broader phenomenon that begins long before lesions manifest, and behavioural problems caused by various stressors present themselves weeks before they escalate to damaging behaviour, resulting in serious injuries. Therefore, detailed data on behaviours, which can be considered precursors of tail biting, such as oral and nasal manipulation of conspecifics, should be collected. The present data were collected in the course of a large study on the genetic potential of protein efficiency, in which the crude protein content in the diet was reduced to 80% of the recommendations. Dietary protein reduction is a promising way to reduce nitrogen emissions in pig manure, but its implications for animal welfare are not yet clear. Pigs differ phenotypically and genetically in their ability to utilise dietary proteins; therefore, there might be individual differences in how they cope with the protein reduction. Here, we present detailed data of focal observations of 95 pigs at an experimental farm with undocked tails that were fed a protein-reduced diet. Pigs were observed directly in their home pens for 5 min each on four different days. All actions directed towards objects in the pen, interactions with and confrontations among pen mates, and straw rooting behaviour and general activity were recorded. After the behavioural observations, wounds on different parts of the body and the cleanliness of the pigs were noted. The protein efficiency of 94 pigs was obtained. The data set comprises six tables. The first table contains information on the animals, including the identities of their parents, farrowing group, sex, and protein efficiency. The other data tables contain four 5-min observations of each pig on 10 object-manipulation behaviours; 150 interaction behaviours, including reactions; 14 confrontation behaviours and their outcomes and reactions; 10 mounting behaviours, including reactions; two rooting behaviours; seven basic behaviours; and an index of general activity. The observations took place under comparatively good housing conditions. Pigs were not tail-docked and were given fresh straw daily, <em>ad libitum</em> access to feed, floor space above the legal requirements (only a partially slatted floor), and daily cleaning of pens, and they were closely monitored for signs of damaging behaviour; all of these are favourable conditions as they limit stress and the risk of damaging behaviour. These data can be used to further explore the relationships of specific behaviours and phenomena and their association with protein efficiency. The ethogram can be used as a template for further observations. Practitioners could use the data to support pigs’ need for occupation, such as by providing sufficient straw.</p>
Manipulation of complex objects dataset - Rhythmic Cup Task experiment
<p>This dataset contains raw data from a behavioral neuroscience experiment conducted at Northeastern University, Boston, MA, USA.</p> <p>Ten participants rhythmically manipulated a virtual cup containing a ball via a robotic manipulandum which controlled the cup position and provided haptic feedback of the force applied by the ball. The cup movement was constrained to 1 dimension. Participants were free to choose the frequency of oscillation, while the amplitude was imposed by visual constraints. The cup and ball system was represented mathematically by a cart and pendulum system. The following dimensions were used for the experiment: pendulum length = 0.45m, pendulum mass = 0.6kg, cart mass = 2.4kg.</p> <p>Each participant performed 5 blocks of 10 trials. For each data file, the first 2 letters identify (anonymously) a participant, the first number identifies the block and the second number identifies the trial within the block.</p> <p>The data are in Matlab data format. "RackPosition" (resp. Velocity, Acceleration) corresponds to the cart position (resp. velocity, acceleration). Y is the axis of the movement. "BallTheta" and "BallOmega" are the pendulum angular position and velocity. Please note that in this dataset angles are positive in clockwise direction (contrary to usual mechanical conventions). "BallForce" is the force applied by the pendulum on the cart.</p> <p>This experimental dataset was confronted to simulation results obtained with two different models. One ("Uncoupled Model") only simulates the dynamics of a cart and pendulum system (Matlab script "cup_task_uncoupled_model_inverde_dynamics.m" and "simulation_cup_task_uncoupled_model_inverse_dynamics.m"). Simulations of this Uncoupled Model are run using inverse dynamics, assuming a sinusoidal trajectory of the cart. The second model ("Coupled Model") includes a simplified model of hand dynamics, represented by an ideal force generator in parallel with a spring and a damper to simulate hand impedance (Matlab and Simulink scripts "cup_task_coupled_model.slx" and "simulation_coupled_model_forward_dynamics.m"). Simulations of this Coupled Model are run using forward dynamics</p> <p> </p>
Multimodal Sensory Learning for Object Manipulation
<p><strong>Multimodal Manipulation Learning Database</strong></p> <p>The dataset consists of data recordings for object manipulation with audio-tactile sensory feedback for object handover. It captures the auditory and tactile signals of a Kuka IIWA robot with an Allegro hand holding a plastic container containing different materials. The robot manipulates the container with vertical shaking and rotation motions. The data consists of force/pressure measurements on the Allegro hand using a Tekscan tactile skin sensor, auditory signals from a microphone, and the joints data of the IIWA robot and the Allegro hand joints. </p> <p><strong>Dataset</strong></p> <p>Each datafile is a rosbag file containing the data recording from one trial of a robot motion with one material, with rostopics on the following data:</p> <ul> <li>Kuka IIWA 7 Joint data: /iiwa/TorqueController/command /iiwa/eePose /iiwa/joint_states</li> <li>Allegro hand joint data: /allegro_hand_right/joint_states</li> <li>Tekscan sensor recording (tactile force/pressure sensor data on hand): /tekscan/frame</li> <li>Audio data (for microphone attached to hand): /audio/audio /audio/audio_info</li> <li>Experiment information: /trialInfo <ul> <li>which contains: <ul> <li>trial information (motion type, speed, etc.)</li> <li>start/stop of different phases of the trials</li> </ul> </li> </ul> </li> </ul> <p><strong>Motion Types</strong></p> <p>The database contains recordings for the robot executing two different motion types: vertical shaking of the object and rotation of the object.</p> <p><strong>Materials</strong></p> <p>The database contains recordings for 5 different material classes in the plastic container, as shown below: empty, vitamins, gummies, cornflakes, and rice. We used approximately the same volume of each material for each trial. We tested each material class and motion combination for a total of 10 different experimental conditions and collected 30 trials for each condition.</p> <p>The vertical motion dataset was entirely collected on 2021/08/25. The rotation dataset was split into two day. The empty, gummies and rice class data was collected on 2021/08/26. The vitamins and cornflakes classes were collected on 2021/09/13.</p> <p><strong>Database Setup</strong></p> <p>The database consists of the data in two formats: annotated ('annotated_bags_mml.zip') and unannotated/numbered filenames ('numbered_bags_mml.zip') datasets. The data in the two datasets are identical- the annotated filename dataset has the experimental descriptions in the filename directly (as described below).</p> <p>The annotated filenames dataset ('annotated_bags_mml.zip') consists of a single directory with all 300 rosbag datafiles (10 experimental conditions, 30 trials each). Each rosbag (<code>.bag</code>) is saved in the directory, with filename specified ('Date Recorded YYYYMMDD' + '_motion' + '_material' + '_trialID' + '.bag'). Motion Types are: {'vertical', 'rotation'}. Materials are: {'empty', 'cornflakes', 'gummies', 'rice', 'vitamins'}. For each experimental condition, there are 30 datafiles with trial IDs from 0-29.</p> <p>All data recordings for the vertical motion have filenames: '20210825_vertical_+ 'material' + 'trialID' +'.bag). For the rotation motion, the empty, gummy and rice classes have filenames: '20210826_rotation_+ 'material' + 'trialID' +'.bag). For cornflakes and vitamins classes, the filenames are: '20210913_rotation_+ 'material' + 'trialID' +'.bag).</p> <p>The numbered/unannotated file dataset ('numbered_bags_mml.zip') consists of the same 300 data files as in the annotated dataset except here the filenames are numbered '{000-299}.bag'. The directory contains a spreadsheet ('annotations.csv') listing the experimental descriptions for each file name. The columns of the xls spreadsheet are {'Bagfile name', 'Year', 'Month', 'Day', 'Motion/Movement (mvt_type)', 'Material', 'Trial ID'}, where {Year, Month, Day} refer to the date that trial data was collected (either 2021/08/25, 2021/08/26, or 2021/09/13). </p>
Yolo object detector raw results for example manipulations
<p>There is one example of the direct object detection outputs from YOLO for each manipulation type. Should you sish for more datasets, please contact the authors</p>
AnDy Data - Human Human Object Co-Manipulation
<p>This dataset contains measurements for two manipulation experiments. The first is a human dyad executing a shared co-manipulation task. And the second is a human single-handedly executing the same task in the same experimental setup.</p> <p>Both experiments are described in detail in the provided .PDF file. The participants, and the conditions in which they executed the experiments are described in the accompanying .XLS files.</p> <p>With the exception of proprietary data, all files are in .CSV format. The collected data is comprised of: Kinematic data of the subjects' arm; raw electromyography (EMG) signals from the subjects' arm; as well as the maximum value of contraction for each measured muscle in their arm. Additionally, there is data regarding the efficiency in which the dyad executes the task (number of wall touches).</p> <p>The Kinematic, and the EMG data, were collected with proprietary software, so the original files in the proprietary format are also made available.</p>
Infant s Examination and Manipulation of Objects
ClinicalTrials.gov study NCT00053469. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Results of visual servoing architecture of mobile manipulators for precise industrial operations on moving objects
<p>Results obtained during the experiment of screw fastening on moving objects using a mobile manipulator.</p>
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.