We present a set of repositories including the essential parts for a general semantic active perception system. The tools are related to: Neural Networks and Deep Learning, sensors bridges, semantic mapping, informative path planning, the airsim simulator, and deployment on a turtlebot robot.

https://github.com/dvdmc/active_perception

This repository contains all the supplementary material associated to the paper with title "Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems". Please check out our project website for more details:

 https://eduardosebastianrodriguez.github.io/phMARL/

We present a novel neural network that combines self-attention and a convex optimization algorithm to learn to identify the graph structure of a multi-robot or multi-agent task. Check the repository if you want to develop your neural network or replicate our experiments.

https://github.com/EduardoSebastianRodriguez/LIGMRS

We present LEMURS, but not the animal: this is a general framework for learning distributed multi-robot interactions. Check the repository if you want to train your own multi-robot policies from demonstrations or replicate the experiments from our papers.

https://github.com/EduardoSebastianRodriguez/LEMURS

CineMPC is ready to play on a drone platform using ROS. The infrastructure to test it in the photorealistic simulator AirSim is provided as an example of use.

https://github.com/ppueyor/CineMPC_ros

CinemAirsim is a plugin for the Airsim simulator that enables the use of cinematographic cameras onboard the robots, offering the possibility to control in real time their intrinsic parameters.

https://github.com/ppueyor/CinemAirSim

If you want to try to herd your own group of evaders, check our repository. It contains the codes associated to different multi-robot policies, based on Implicit Control, and the different simulations and experiments found in our multi-robot herding papers.

https://github.com/EduardoSebastianRodriguez/Multi-Robot-Implicit-Control-Herd