Causal Navigation by Continuous-time Neural NetworksDownload PDF

21 May 2021, 20:47 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Continuous-time neural networks, Causality, neural ODEs, continuous-depth models, visual navigation
  • TL;DR: We showed that a particular subclass of continuous-time neural network models are dynamic causal models that can perform interpretable, causal and robust visual navigation.
  • Abstract: 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.
  • Supplementary Material: pdf
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  • Code: https://github.com/mit-drl/deepdrone
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