---
res:
  bibo_abstract:
  - "Adversarial training (i.e., training on adversarially perturbed input data) is
    a well-studied method for making neural networks robust to potential adversarial
    attacks during inference. However, the improved robustness does not\r\ncome for
    free but rather is accompanied by a decrease in overall model accuracy and performance.
    Recent work has shown that, in practical robot learning applications, the effects
    of adversarial training do not pose a fair trade-off\r\nbut inflict a net loss
    when measured in holistic robot performance. This work revisits the robustness-accuracy
    trade-off in robot learning by systematically analyzing if recent advances in
    robust training methods and theory in\r\nconjunction with adversarial robot learning
    can make adversarial training suitable for real-world robot applications. We evaluate
    a wide variety of robot learning tasks ranging from autonomous driving in a high-fidelity
    environment\r\namenable to sim-to-real deployment, to mobile robot gesture recognition.
    Our results demonstrate that, while these techniques make incremental improvements
    on the trade-off on a relative scale, the negative side-effects caused by\r\nadversarial
    training still outweigh the improvements by an order of magnitude. We conclude
    that more substantial advances in robust learning methods are necessary before
    they can benefit robot learning tasks in practice.@eng"
  bibo_authorlist:
  - 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: Alexander
      foaf_name: Amini, Alexander
      foaf_surname: Amini
  - foaf_Person:
      foaf_givenName: Daniela
      foaf_name: Rus, Daniela
      foaf_surname: Rus
  - foaf_Person:
      foaf_givenName: Thomas A
      foaf_name: Henzinger, Thomas A
      foaf_surname: Henzinger
      foaf_workInfoHomepage: http://www.librecat.org/personId=40876CD8-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2985-7724
  bibo_doi: 10.48550/arXiv.2204.07373
  dct_date: 2022^xs_gYear
  dct_language: eng
  dct_title: Revisiting the adversarial robustness-accuracy tradeoff in robot learning@
...