Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Inge M. Hootsmans"'
Autor:
Pablo Barros, Anne C. Bloem, Inge M. Hootsmans, Lena M. Opheij, Romain H. A. Toebosch, Emilia Barakova, Alessandra Sciutti
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to th
Externí odkaz:
https://doaj.org/article/a79012560f524be997069af0fbe9ca89
Autor:
Pablo V. A. Barros, Inge M. Hootsmans, Emilia I. Barakova, Romain H. A. Toebosch, Lena M. Opheij, Alessandra Sciutti, Anne C. Bloem
Publikováno v:
HRI 2021-Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 524-528
STARTPAGE=524;ENDPAGE=528;TITLE=HRI 2021-Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
HRI (Companion)
Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
STARTPAGE=524;ENDPAGE=528;TITLE=HRI 2021-Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
HRI (Companion)
Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
This paper describes the design of an interactive game between humans and a robot that makes it possible to observe, analyze, and model competitive strategies and affective interactions with the aim to dynamically generate appropriate responses or in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e2ff38fe3c3c297e2588298844a04026
https://research.tue.nl/nl/publications/016b97c9-d6ad-4a33-b726-6c04517d2b8d
https://research.tue.nl/nl/publications/016b97c9-d6ad-4a33-b726-6c04517d2b8d
Autor:
Inge M. Hootsmans, Emilia I. Barakova, Pablo V. A. Barros, Matthias Kerzel, Romain H. A. Toebosch, Anne C. Bloem, Lena M. Opheij
Publikováno v:
HRI 2020-Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 142-144
STARTPAGE=142;ENDPAGE=144;TITLE=HRI 2020-Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
HRI (Companion)
STARTPAGE=142;ENDPAGE=144;TITLE=HRI 2020-Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
HRI (Companion)
This paper is the first step of an attempt to equip social robots with emotion recognition capabilities comparable to those of humans. Most of the recent deep learning solutions for facial expression recognition under-perform when deployed in Human-R
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::043800693729f87b6d1fe394d25291dc
https://research.tue.nl/en/publications/95c7f8d4-64ee-458f-99a0-5c4fe8bfedce
https://research.tue.nl/en/publications/95c7f8d4-64ee-458f-99a0-5c4fe8bfedce
Autor:
Pablo V. A. Barros, Emilia I. Barakova, Alessandra Sciutti, Lena M. Opheij, Inge M. Hootsmans, Anne C. Bloem, Romain H. A. Toebosch
Publikováno v:
Frontiers in Robotics and AI, Vol 8 (2021)
Frontiers in Robotics and AI
Frontiers in Robotics and AI, 8:669990. Frontiers Media S.A.
Frontiers in Robotics and AI
Frontiers in Robotics and AI, 8:669990. Frontiers Media S.A.
Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01ce20abae515b9e1475f4af2f581bf4