RL Collision Avoidance provides a complete pipeline for developing, training, and deploying multi-robot collision avoidance policies. It offers a set of Gym-compatible simulation scenarios where agents learn collision-free navigation through reinforcement learning algorithms. Users can customize environment parameters, leverage GPU acceleration for faster training, and export learned policies. The framework also integrates with ROS for real-world testing, supports pre-trained models for immediate evaluation, and features tools for visualizing agent trajectories and performance metrics.

Aurora Innovation specializes in creating cutting-edge AI technology for self-driving vehicles. Their systems leverage deep learning and robotics to enhance perception, planning, and control, enabling cars to navigate safely and efficiently in various conditions. Aurora’s software integrates with existing vehicle platforms, offering manufacturers a reliable route to autonomy while focusing on real-world testing and safety.

Autoware is a cutting-edge open-source software platform designed for autonomous vehicle functions. It integrates various capabilities such as perception, localization, planning, and control, catering to the needs of developers and researchers. With Autoware, users can create sophisticated autonomous driving applications, accessing a wide array of tools and pre-configured software modules, facilitating rapid testing and deployment in real-world environments.