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The dataset contains the following data from successful and failed executions of the Toyota HSR robot placing a book on a shelf.
RGB images from the robot's head camera
Depth images from the robot's head camera
Rendered images of the robot's 3D model from the point of view of the robot's head camera
Force-torque readings from a wrist-mounted force-torque sensor
Joint efforts, velocities and positions
extrinsic and intrinsic camera calibration parameters
frame-level anomaly annotations
The anomalies that occur during execution include:
the manipulated book falling down
books on the shelf being disturbed significantly
camera occlusions
robot being disturbed by an external collision
The dataset is split into a train, validation and test set with the following number of trials:
Train: 48 successful trials
Validation: 6 successful trials
Test: 60 anomalous trials and 7 successful trials
Short summary
Accompanying dataset for our paper
A. Mitrevski, P. G. Plöger, and G. Lakemeyer, "Robot Action Diagnosis and Experience Correction by Falsifying Parameterised Execution Models," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2021.
Contents
The dataset includes a single zip archive, containing data from the experiment described in the paper (conducted with a Toyota HSR). The zip archive contains three subdirectories:
handle_grasping_failure_database: A dump of a MongoDB database containing data from the handle grasping experiment, including ground-truth grasping failure annotations
pre_arm_motion_images: Images collected from the robot's hand camera before moving the robot's hand towards the handle
pregrasp_images: Images collected from the robot's hand camera just before closing the gripper for grasping
The image names include the time stamp at which the images were taken; this allows matching each image with the execution data in the database.
Database usage
After unzipping the archive, the database can be restored with the command
mongorestore handle_grasping_failure_database
This will create a MongoDB database with the name drawer_handle_grasping_failures.
Code for processing the data and failure analysis can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
Contents
There are two zip archives included (grasping.zip and throwing.zip), corresponding to two experiments (grasping objects and throwing them in a drawer), both performed with a Toyota HSR. Each archive contains two directories - learning and generalisation - with object-specific learning and generalisation data. For each object, we provide a dump of a MongoDB database, which contains data sufficient for learning the models used in our experiments.
Usage
After unzipping the archives, each database can be restored with the command
mongorestore [data_directory_name]
This will create a MongoDB database with the name of the directory. Code for processing the data and model learning can be found in our <a href="https://github.com/alex-mitrevski/explainable-robot-execution-models">GitHub repository.
Here we provide the electrophysiology data for the manuscript "Two functional epithelial sodium channel isoforms are present in rodents despite pronounced evolutionary pseudogenization and exon fusion", published in Molecular Biology and Evolution (2021): msab271 (doi: 10.1093/molbev/msab271). Data are reported as current values in Excel format, sorted according to the appearance in Figures and supplemented by explanatory text on the procedures/data presentation.