Manipulation of Granular Materials by Learning Particle Interactions
Our article “Manipulation of Granular Materials by Learning Particle Interactions” was just accepted to IEEE Robotics and Automation Letters.
In this work we propose to use a Graph Neural Network (GNN) surrogate model to learn the particle interactions of granular materials. We perform planning of manipulation trajectories with the learnt surrogate model to arrange the material into a desired configuration.
For more information you can also visit the project website.
The code is also available open source.