---
res:
  bibo_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\r\ndeep 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.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Charles J
      foaf_name: Vorbach, Charles J
      foaf_surname: Vorbach
  - foaf_Person:
      foaf_givenName: Ramin
      foaf_name: Hasani, Ramin
      foaf_surname: Hasani
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Amini, Alexander
      foaf_surname: Amini
  - foaf_Person:
      foaf_givenName: Mathias
      foaf_name: Lechner, Mathias
      foaf_surname: Lechner
      foaf_workInfoHomepage: http://www.librecat.org/personId=3DC22916-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Daniela
      foaf_name: Rus, Daniela
      foaf_surname: Rus
  dct_date: 2021^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1049-5258
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Causal navigation by continuous-time neural networks@
...