  Simulators are powerful tools for autonomous robot learning as they offer
scalable data generation, flexible design, and optimization of trajectories.
However, transferring behavior learned from simulation data into the real world
proves to be difficult, usually mitigated with compute-heavy domain
randomization methods or further model fine-tuning. We present a method to
improve generalization and robustness to distribution shifts in sim-to-real
visual quadrotor navigation tasks. To this end, we first build a simulator by
integrating Gaussian Splatting with quadrotor flight dynamics, and then, train
robust navigation policies using Liquid neural networks. In this way, we obtain
a full-stack imitation learning protocol that combines advances in 3D Gaussian
splatting radiance field rendering, crafty programming of expert demonstration
training data, and the task understanding capabilities of Liquid networks.
Through a series of quantitative flight tests, we demonstrate the robust
transfer of navigation skills learned in a single simulation scene directly to
the real world. We further show the ability to maintain performance beyond the
training environment under drastic distribution and physical environment
changes. Our learned Liquid policies, trained on single target manoeuvres
curated from a photorealistic simulated indoor flight only, generalize to
multi-step hikes onboard a real hardware platform outdoors.
