Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Harold Soh"'
Autor:
Kurt Gray, Kai Chi Yam, Pok Man Tang, David De Cremer, Yochanan E. Bigman, Remus Ilies, Harold Soh
Publikováno v:
Journal of Applied Psychology. 106:1557-1572
Organizations are increasingly relying on service robots to improve efficiency, but these robots often make mistakes, which can aggravate customers and negatively affect organizations. How can organizations mitigate the frontline impact of these robo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7619995cb7b405520889dcb00897d836
https://doi.org/10.1037/apl0000834
https://doi.org/10.1037/apl0000834
Autor:
Cynthia Matuszek, Harold Soh, Matthew Gombolay, Nakul Gopalan, Reid Simmons, Stefanos Nikoladis
Publikováno v:
2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ac41febe9174ccbd1e575df5e1bfdf2
https://doi.org/10.1109/hri53351.2022.9889367
https://doi.org/10.1109/hri53351.2022.9889367
Publikováno v:
Journal of Experimental Social Psychology. 102:104360
Robots are transforming organizations, with pundits forecasting that robots will increasingly perform managerial tasks. One such key managerial task is the evaluation and delivery of feedback regarding an employee’s performance, including negative
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::509cc4211325f38cb690941e0c18009c
https://doi.org/10.1016/j.jesp.2022.104360
https://doi.org/10.1016/j.jesp.2022.104360
Publikováno v:
HRI (Companion)
Recent advances in robot capabilities have led to a growing consensus that robots will eventually be deployed at scale across numerous application domains. An important open question is how humans and robots will adapt to one another over time. In th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::793e60f521bfdda7ec404a6aaab22276
https://doi.org/10.1145/3434074.3447220
https://doi.org/10.1145/3434074.3447220
Publikováno v:
ICRA
This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities; this can cause poor performance on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db3dc59e17a58169277de2e484feedae
Publikováno v:
ICRA
Sequences and time-series often arise in robot tasks, e.g., in activity recognition and imitation learning. In recent years, deep neural networks (DNNs) have emerged as an effective data-driven methodology for processing sequences given sufficient tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66e22d06f3c2e8136898525663ad7e84
Publikováno v:
IROS
Touch is arguably the most important sensing modality in physical interactions. However, tactile sensing has been largely under-explored in robotics applications owing to the complexity in making perceptual inferences until the recent advancements in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::911a406f44d86ea2912c780142697de8
https://doi.org/10.1109/iros45743.2020.9340693
https://doi.org/10.1109/iros45743.2020.9340693
Publikováno v:
IROS
Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57ccee8bdd3b753c24de0b1c414564d5
https://doi.org/10.1109/iros45743.2020.9340736
https://doi.org/10.1109/iros45743.2020.9340736
Publikováno v:
IROS
Tactile perception is crucial for a variety of robot tasks including grasping and in-hand manipulation. New advances in flexible, event-driven, electronic skins may soon endow robots with touch perception capabilities similar to humans. These electro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4e914b0c5b0ec7a541dcb7a5f1f11cb
http://arxiv.org/abs/2008.08046
http://arxiv.org/abs/2008.08046
Publikováno v:
CVPR
Implicit Generative Models (IGMs) such as GANs have emerged as effective data-driven models for generating samples, particularly images. In this paper, we formulate the problem of learning an IGM as minimizing the expected distance between characteri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed542fd8e6b2305858a14884a5c296cb
https://doi.org/10.1109/cvpr42600.2020.00750
https://doi.org/10.1109/cvpr42600.2020.00750