https://github.com/autolabmodena/autolab-co-manipulation-dataset

A dataset of Franka Emika Panda recordings of 2D symbols

https://github.com/autolabmodena/autolab-co-manipulation-dataset

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A dataset of Franka Emika Panda recordings of 2D symbols

Basic Info
  • Host: GitHub
  • Owner: AutoLabModena
  • License: mit
  • Language: MATLAB
  • Default Branch: main
  • Size: 235 MB
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README.md

AutoLab-Co-Manipulation-Dataset

A dataset of Robot recordings of 2D symbols.

dataset

Welcome to the AutoLab Co-Manipulation Dataset page!
This is a public available dataset and comprises of 21 different 2D symbols. Each symbol was printed in A4 format, then traced back with the help of a Franka Emika Panda robot, equipped with a Schunk FT-AXIA force/torque sensor.
Six recordings were made for each symbols, by fixing the start and end point to be always the same. In the provided files.pdf related to the symbols, start and end points are represented, respectively, by a circle and an asterisk.

A key feature of the dataset is that symbol's recordings of the same group do not differ in path, but instead vary significantly in time, including speed variations and pauses.
The recordings are provided both as MATLAB and Python structures in every symbol folder, named respectively as symbol_data.mat and symbol_data.npy . The structure is the following:

symboldata: structure of 6 elements with fields
--- symbol
data[i].pos : end-effector position recordings [3xT]
--- symboldata[i].vel : end-effector velocity recordings [3xT]
--- symbol
data[i].F : end-effector force recordings [3xT]

where T defines the number of samples for each recording.

Load MATLAB file example: matlab load('symbol_data.mat'); i = 2; % 2nd recording pos = symbol_data(i).pos; vel = symbol_data(i).vel; F = symbol_data(i).F; Load Python file example: python import numpy as np symbol_data = np.load('symbol_data.npy') i = 2 # 2nd recording pos = symbol_data[i]['pos'] vel = symbol_data[i]['vel'] F = symbol_data[i]['F']


The AutoLab Co-Manipulation Dataset was introducted as part of the paper entitled Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations by G.Braglia, D.Tebaldi, A.E.Lazzaretti and L.Biagiotti, from University of Modena and Reggio Emilia and Federal Technological University of Paran.
If you find this dataset useful for your work/research, please cite: bibtex @misc{braglia2024arclengthbasedwarpingrobotskill, title={Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations}, author={Giovanni Braglia and Davide Tebaldi and Andr Eugenio Lazzaretti and Luigi Biagiotti}, year={2024}, eprint={2410.13322}, archivePrefix={arXiv}, primaryClass={cs.RO}, url={https://arxiv.org/abs/2410.13322}, }

Folders

  • AutoLabCoManipulationData/ : dataset files in .mat and .npy format, for each symbol a .pdf file depicting the respective symbol is included;
  • load_example.m : a MATLAB code to plot symbol recordings;
  • load_example.py : a Python code to plot symbol recordings;

Questions & Suggestions

For any doubt, question or suggestion, please feel free to email at: giovanni.braglia@unimore.it

Owner

  • Name: AutoLab
  • Login: AutoLabModena
  • Kind: user
  • Location: Modena
  • Company: University of Modena and Reggio Emilia

Automatic Control group of the engineering department "Enzo Ferrari", University of Modena and Reggio Emilia

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