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Documentation Index

Fetch the complete documentation index at: https://docs.generalrobotics.dev/llms.txt

Use this file to discover all available pages before exploring further.

AirGen is a next-generation simulator focused on enabling General Robot Intelligence through large-scale data generation and evaluation for machine learning. Built on top of AirSim, AirGen supports a broad spectrum of robots—including aerial, wheeled, quadruped, and perception-only agents—in diverse environments such as urban settings, industrial areas, and natural landscapes. AirGen Overview To increase the ease of development, a Python API is integrated with GRID to allow for seamless development while also being exposed to allow the user full control over the simulation environment and robotic agents from GRID. In this section we will cover the key features of Airgen as well as document the API.
The client described in Robots is fundamental to interacting with the airgen simulation; it is highly recommended to read this page first

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Getting Started Roadmap

  • Start with Robots & AirGen Client to understand the client hierarchy and robot_name usage.
  • Configure your fleet through Configuration Settings.
  • Explore individual capabilities in Features (cameras, sensors, navigation).
  • Dive into Examples for end-to-end notebooks (drone, car, and data generation workflows).

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Key Features

  • Unreal Engine 5 Ready: AirGen is built on top of Unreal Engine 5, enabling you to leverage the latest features in Unreal Engine such as Nanite, Lumen, and more.
  • Geospecific Environment Support: Dive into real-world terrains and photorealistic tilesets with AirGen’s support for geospecific environments powered by the Cesium platform. Experience a seamless blend of the virtual and real world, delivering unparalleled experiences for your simulation needs.
  • Heterogeneous Multi-Agent Control: Drive drones, cars, quadrupeds, and computer-vision agents together from a unified API. Each robot is addressed by robot_name, enabling fully independent control for mixed fleets without switching simulation modes.
  • Advanced Path / Trajectory Planning: Leveraging features such as occupancy maps, signed distance fields, path planning algorithms, and minimum snap trajectory generation, AirGen enables generating optimal and collision-free trajectories at scale.
  • Large-Scale Data Collection: AirGen’s support for large-scale data collection allows you to collect virtually infinite amounts of photorealistic multimodal data, unlocking the training of generalizable perception-action models.
  • Digital Twin Import: AirGen supports importing meshes or scenes at runtime, enabling you to simulate real-world context within the simulation.