Learning Vision-based Agile Flight via Differentiable Physics - Raw Data Presented in the Figures

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 previ-
ous 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.