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.
模拟器是自主机器人学习的强大工具，因为它们提供了可扩展的数据生成、灵活的设计和轨迹优化。然而，将从模拟数据中学到的行为转移到现实世界往往是困难的，通常通过计算密集的领域随机化方法或进一步的模型微调来缓解。我们提出了一种方法，以改善从模拟到真实视觉四旋翼导航任务中的泛化能力和对分布偏移的鲁棒性。为此，我们首先通过将高斯飞溅与四旋翼飞行动力学整合来构建模拟器，然后使用液态神经网络训练鲁棒的导航策略。通过这种方式，我们获得了一个全栈的模仿学习协议，该协议结合了3D高斯飞溅辐射场渲染的进展、精巧的专家演示训练数据编程和液态网络的任务理解能力。通过一系列定量飞行测试，我们展示了在单一模拟场景中学到的导航技能直接鲁棒地转移到真实世界的能力。我们进一步展示了在剧烈分布和物理环境变化下保持超出训练环境性能的能力。我们的液态政策学习，仅在从逼真的模拟室内飞行中策划的单一目标机动上训练，普遍适用于在真实硬件平台上户外的多步远足。