Causal Navigation by Continuous-time Neural Networks
This work addresses domain shift issues in imitation learning for drone navigation, offering a novel approach that could improve generalization in photorealistic environments.
The paper tackled the problem of imitation learning in vision-based navigation by proposing a framework using continuous-time neural networks to learn causal representations, which enabled robust navigation tasks where advanced recurrent models failed.
Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-time neural models and face difficulties in generalizing to domain shifts by failing to account for the causal relationships between the agent and the environment. In this paper, we propose a theoretical and experimental framework for learning causal representations using continuous-time neural networks, specifically over their discrete-time counterparts. We evaluate our method in the context of visual-control learning of drones over a series of complex tasks, ranging from short- and long-term navigation, to chasing static and dynamic objects through photorealistic environments. Our results demonstrate that causal continuous-time deep models can perform robust navigation tasks, where advanced recurrent models fail. These models learn complex causal control representations directly from raw visual inputs and scale to solve a variety of tasks using imitation learning.