ILVS - Imitation Learning for Visual Servoing

This package provides code and a dataset to test imitation learning approaches on an image-based visual servoing benchmark.

It also implements two of the approaches described in (Paolillo and Saveriano, 2022) to embed a visual seervoing task into stable dynamical systems.

Demos description

• augment_LASA_dataset.m: a script to augment the LASA Handwriting dataset with image features.
• demo_LASA_VS_CLFDM.m: a demo to run CLFDM on the augmented LASA Handwritten dataset.
• demo_LASA_VS_RDS.m: a demo to run RDS on the augmente LASA Handwritten dataset.

Software Requirements

The code is developed and tested under Ubuntu 18.04 and Matlab2019b.

References

Please acknowledge the authors in any academic publication that used parts of these codes.

@inproceedings{paolillo2020learning,
	author = {Paolillo, A. and Saveriano, M.},
	booktitle = {IEEE International Conference on Robotics and Automation},
	title = {Learning Stable Dynamical Systems for Visual Servoing},
	year = {2022}
}

Third-party material

Third-party code and dataset have been included in this repository for convenience.

• LASA Handwriting dataset: please acknowledge the authors in any academic publications that have made use of the LASA HandWritten dataset by citing: S. M. Khansari-Zadeh and A. Billard, "Learning Stable Non-Linear Dynamical Systems with Gaussian Mixture Models", IEEE Transaction on Robotics, 2011.

• GMR: please acknowledge the authors in any academic publications that have made use of the GMR library by citing: S. Calinon et al., "On Learning, Representing and Generalizing a Task in a Humanoid Robot", IEEE Transactions on Systems, Man and Cybernetics, Part B., 2006.

• CLFDM: please acknowledge the authors in any academic publications that have made use of the CLFDM library by citing: S.M. Khansari-Zadeh and A. Billard, "Learning Control Lyapunov Function to Ensure Stability of Dynamical System-based Robot Reaching Motions" Robotics and Autonomous Systems, 2014.

• RDS: please acknowledge the authors in any academic publications that have made use of the RDS code by citing: M. Saveriano and D. Lee, "Incremental skill learning of stable dynamical systems" IEEE International Conference on Intelligent Robots and Systems, 2018.

Note

This source code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY.