  Swarm navigation in cluttered environments is a grand challenge in robotics.
This work combines deep learning with first-principle physics through
differentiable simulation to enable autonomous navigation of multiple aerial
robots through complex environments at high speed. Our approach optimizes a
neural network control policy directly by backpropagating loss gradients
through the robot simulation using a simple point-mass physics model and a
depth rendering engine. Despite this simplicity, our method excels in
challenging tasks for both multi-agent and single-agent applications with
zero-shot sim-to-real transfer. In multi-agent scenarios, our system
demonstrates self-organized behavior, enabling autonomous coordination without
communication or centralized planning - an achievement not seen in existing
traditional or learning-based methods. In single-agent scenarios, our system
achieves a 90% success rate in navigating through complex environments,
significantly surpassing the 60% success rate of the previous state-of-the-art
approach. Our system can operate without state estimation and adapt to dynamic
obstacles. In real-world forest environments, it navigates at speeds up to 20
m/s, doubling the speed of previous imitation learning-based solutions.
Notably, all these capabilities are deployed on a budget-friendly $21 computer,
costing less than 5% of a GPU-equipped board used in existing systems. Video
demonstrations are available at https://youtu.be/LKg9hJqc2cc.
