This package implements an ergodic controller on point clouds and it is the supplementary material of the paper "Tactile Ergodic Coverage on Curved Surfaces"
Link to the paper: http://arxiv.org/abs/2402.04862
Paper webpage including interactive plots and real-world experiment videos:
https://sites.google.com/view/tactile-ergodic-control/
Notebooks:
-
tactile_ergodic_control.ipynb
- A notebook describing the whole method from start to finish with references and equations.
-
Google colab version you can play with this without installing anything to your computer:
Utilities:
- pointcloud_utils.py
- Point cloud operations such as read/write kNN queries, gradient computation, etc.
- plotting_utils.py
- A collection of plotting utility functions used by the notebooks.
- virtual_agents.py
- Classes for the first and second order virtual agents.
Point clouds:
- Stanford Bunny from the original dataset bun270.ply, 'X' image projected to it using set_point_cloud_target.ipynb
- A random cup we found in the office and recorded using our setup, 'X' image projected to it using set_point_cloud_target.ipynb
- A plate from IKEA, recorded using our setup (it includes the exploration target by itself)
To compute the discrete Laplacian on point clouds (and also meshes if you want) robust_laplacian: https://github.com/nmwsharp/robust-laplacians-py
@article{Sharp:2020:LNT,
author={Nicholas Sharp and Keenan Crane},
title={{A Laplacian for Nonmanifold Triangle Meshes}},
journal={Computer Graphics Forum (SGP)},
volume={39},
number={5},
year={2020}
}
For geometric algebra operations: pygafro: https://gitlab.idiap.ch/tloew/gafro
@article{loewGeometricAlgebraOptimal2023,
title = {Geometric {{Algebra}} for {{Optimal Control}} with {{Applications}} in {{Manipulation Tasks}}},
author = {L\"ow, Tobias and Calinon, Sylvain},
date = {2023},
journal = {IEEE Transactions on Robotics},
doi = {10.1109/TRO.2023.3277282}
}
For basic point cloud operations (another library can be easily used instead): open3d: https://www.open3d.org
For plotting and point cloud visualizations: plotly: https://plotly.com
For sparse matrix operations: scipy: https://scipy.org
For linear algebra operations: numpy: https://numpy.org
Please see the LICENSE for more information.
Contact: cem.bilaloglu@epfl.ch