World Generation
Describe a building in plain text and Arena generates the floorplan, furnishes it, and turns it into a world every simulator can load, or author multi-floor worlds with doors and elevators by hand.
Arena-Rosnav generates human-centric worlds, simulates pedestrians, trains navigation planners, and benchmarks them in Isaac Sim, Gazebo, MuJoCo, and flatsim. It runs on ROS 2 and is open source.
Busy hospital corridor in Isaac Sim
Arena is an open ROS 2 platform for building, running, and comparing robot navigation in realistic indoor spaces full of people, on the simulator of your choice.
Describe a building in plain text and Arena generates the floorplan, furnishes it, and turns it into a world every simulator can load, or author multi-floor worlds with doors and elevators by hand.
Crowds of animated people (staff, visitors, elderly walkers, and wheelchair users) walk, queue, gather, talk, and gesture, and they react to the robot.
Arena fills any world with everyday crowds that fit each room's purpose, for example people at work, on a break, or on a night shift. It then turns plain-language prompts such as "an old man crosses the robot's path", "a power cut leaves the floor in the dark", or "the fire alarm goes off" into ready-to-run test cases. The generated cases bundle straight into a benchmark.

Worlds declare their lights, which every simulator renders and which can dim or fail mid-run. A simulated soundscape lets robots hear footsteps, speech, and alarms through their own microphones.

Train deep reinforcement learning navigation agents in many parallel simulated environments, then deploy the trained policy as a planner in the same worlds.
Arena runs planners through the same worlds, crowds, and tasks, judges every episode, and produces a report that compares them on success, efficiency, safety, social compliance, and energy. Large comparisons can be split across parallel isolated copies of the platform to finish faster.
One world, scenario, and robot definition runs unchanged on four simulators, so you pick the trade-off between visual realism and speed. The images show the same hospital ward in each of them.
Compare classical planners against learned crowd-navigation, visual, and vision-language planners on wheeled, legged, and mobile-manipulator robots.
RA-L 2026 To be presented at ICRA 2027
PEDS treats the pedestrian model as a variable of the benchmark. Robot policy rankings change with the crowd model the policies are tested against.
RSS 2025
We integrate NVIDIA Isaac Gym, benchmark state-of-the-art social navigation planners, and add scenario generation for emergency and rescue situations.
ICRA 2025
We generate worlds from text prompts or floorplans with LLMs and diffusion models, add a semantically annotated 3D model database, and move the platform to ROS 2.
RSS 2024
We add social force models, human-robot interaction patterns, and task modes, and we abstract the platform over Flatland, Gazebo, and Unity.