  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.
