---
_id: '12010'
abstract:
- lang: eng
  text: World models learn behaviors in a latent imagination space to enhance the
    sample-efficiency of deep reinforcement learning (RL) algorithms. While learning
    world models for high-dimensional observations (e.g., pixel inputs) has become
    practicable on standard RL benchmarks and some games, their effectiveness in real-world
    robotics applications has not been explored. In this paper, we investigate how
    such agents generalize to real-world autonomous vehicle control tasks, where advanced
    model-free deep RL algorithms fail. In particular, we set up a series of time-lap
    tasks for an F1TENTH racing robot, equipped with a high-dimensional LiDAR sensor,
    on a set of test tracks with a gradual increase in their complexity. In this continuous-control
    setting, we show that model-based agents capable of learning in imagination substantially
    outperform model-free agents with respect to performance, sample efficiency, successful
    task completion, and generalization. Moreover, we show that the generalization
    ability of model-based agents strongly depends on the choice of their observation
    model. We provide extensive empirical evidence for the effectiveness of world
    models provided with long enough memory horizons in sim2real tasks.
acknowledgement: L.B. was supported by the Doctoral College Resilient Embedded Systems.
  M.L. was supported in part by the ERC2020-AdG 101020093 and the Austrian Science
  Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H. and D.R. were supported
  by The Boeing Company and the Office of Naval Research (ONR) Grant N00014-18-1-2830.
  R.G. was partially supported by the Horizon-2020 ECSEL Project grant No. 783163
  (iDev40) and A.B. by FFG Project ADEX.
article_processing_charge: No
arxiv: 1
author:
- first_name: Axel
  full_name: Brunnbauer, Axel
  last_name: Brunnbauer
- first_name: Luigi
  full_name: Berducci, Luigi
  last_name: Berducci
- first_name: Andreas
  full_name: Brandstatter, Andreas
  last_name: Brandstatter
- first_name: Mathias
  full_name: Lechner, Mathias
  id: 3DC22916-F248-11E8-B48F-1D18A9856A87
  last_name: Lechner
- first_name: Ramin
  full_name: Hasani, Ramin
  last_name: Hasani
- first_name: Daniela
  full_name: Rus, Daniela
  last_name: Rus
- first_name: Radu
  full_name: Grosu, Radu
  last_name: Grosu
citation:
  ama: 'Brunnbauer A, Berducci L, Brandstatter A, et al. Latent imagination facilitates
    zero-shot transfer in autonomous racing. In: <i>2022 International Conference
    on Robotics and Automation</i>. IEEE; 2022:7513-7520. doi:<a href="https://doi.org/10.1109/ICRA46639.2022.9811650">10.1109/ICRA46639.2022.9811650</a>'
  apa: 'Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus,
    D., &#38; Grosu, R. (2022). Latent imagination facilitates zero-shot transfer
    in autonomous racing. In <i>2022 International Conference on Robotics and Automation</i>
    (pp. 7513–7520). Philadelphia, PA, United States: IEEE. <a href="https://doi.org/10.1109/ICRA46639.2022.9811650">https://doi.org/10.1109/ICRA46639.2022.9811650</a>'
  chicago: Brunnbauer, Axel, Luigi Berducci, Andreas Brandstatter, Mathias Lechner,
    Ramin Hasani, Daniela Rus, and Radu Grosu. “Latent Imagination Facilitates Zero-Shot
    Transfer in Autonomous Racing.” In <i>2022 International Conference on Robotics
    and Automation</i>, 7513–20. IEEE, 2022. <a href="https://doi.org/10.1109/ICRA46639.2022.9811650">https://doi.org/10.1109/ICRA46639.2022.9811650</a>.
  ieee: A. Brunnbauer <i>et al.</i>, “Latent imagination facilitates zero-shot transfer
    in autonomous racing,” in <i>2022 International Conference on Robotics and Automation</i>,
    Philadelphia, PA, United States, 2022, pp. 7513–7520.
  ista: 'Brunnbauer A, Berducci L, Brandstatter A, Lechner M, Hasani R, Rus D, Grosu
    R. 2022. Latent imagination facilitates zero-shot transfer in autonomous racing.
    2022 International Conference on Robotics and Automation. ICRA: International
    Conference on Robotics and Automation, 7513–7520.'
  mla: Brunnbauer, Axel, et al. “Latent Imagination Facilitates Zero-Shot Transfer
    in Autonomous Racing.” <i>2022 International Conference on Robotics and Automation</i>,
    IEEE, 2022, pp. 7513–20, doi:<a href="https://doi.org/10.1109/ICRA46639.2022.9811650">10.1109/ICRA46639.2022.9811650</a>.
  short: A. Brunnbauer, L. Berducci, A. Brandstatter, M. Lechner, R. Hasani, D. Rus,
    R. Grosu, in:, 2022 International Conference on Robotics and Automation, IEEE,
    2022, pp. 7513–7520.
conference:
  end_date: 2022-05-27
  location: Philadelphia, PA, United States
  name: 'ICRA: International Conference on Robotics and Automation'
  start_date: 2022-05-23
date_created: 2022-09-04T22:02:02Z
date_published: 2022-07-12T00:00:00Z
date_updated: 2025-09-10T09:39:53Z
day: '12'
department:
- _id: ToHe
doi: 10.1109/ICRA46639.2022.9811650
ec_funded: 1
external_id:
  arxiv:
  - '2103.04909'
  isi:
  - '000941277600124'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2103.04909
month: '07'
oa: 1
oa_version: Preprint
page: 7513-7520
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: Z211
  name: Formal methods for the design and analysis of complex systems
publication: 2022 International Conference on Robotics and Automation
publication_identifier:
  isbn:
  - '9781728196817'
  issn:
  - 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Latent imagination facilitates zero-shot transfer in autonomous racing
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2022'
...