GazeboDomainRandomization
Example application.This is an implementation of some Domain Randomization tools within the ROS+Gazebo framework, following the work of Tobin et al. “Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worl”.
It can be used to generate virtual datasets for an object recognition task of your choice, as it will automatically generate the bounding boxes for the object we seek to recognize in every generated pictures. The object has to be rendered in a .dae file compatible with Gazebo, first.
Kevin Denamganaï
Independent Researcher
My research investigates the conditions under which AI systems acquire and deploy structured symbolic representations — towards in-context grounding of novel atomic symbols and their systematic recombination into unseen configurations — spanning Compositional Generalisation, Formal Mathematics, Differentiable Language Models, and Physical Simulation. I have also investigated Language Emergence & Grounding (Emergent Communication), Unsupervised Representation Learning, Natural Language Processing, and Multi-Agent Deep Reinforcement Learning.