VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multirobot Systems

Virtual object based multi arm- two behavioral motion generator

Multi-Robot Framework (MRF) Software Development Notes | C++, DroneKit API, Linux, OpenCV, Python, ROS

A resilient and adaptive multi-robot target tracking framework with sensing and communication danger zones

The campus of PUCRS University and multiple robots modeled with Gazebo

A platform for long horizontal robotic manipulation and human-robot collaboration research

A risk-aware multi-agent target tracking framework with sensing and communication danger zones

ROS and GAMS/MADARA integration with move_base and Gazebo

A framework offering a scalable, and deadlock-free implementation of prioritised planning with novel customisation options designed to improve performance.