Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Vazquez, Marynel"'
People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However,
Externí odkaz:
http://arxiv.org/abs/2409.07560
Multiclass neural network classifiers are typically trained using cross-entropy loss. Following training, the performance of this same neural network is evaluated using an application-specific metric based on the multiclass confusion matrix, such as
Externí odkaz:
http://arxiv.org/abs/2405.20954
Preference learning has long been studied in Human-Robot Interaction (HRI) in order to adapt robot behavior to specific user needs and desires. Typically, human preferences are modeled as a scalar function; however, such a formulation confounds criti
Externí odkaz:
http://arxiv.org/abs/2403.19795
Autor:
Candon, Kate, Georgiou, Nicholas C., Zhou, Helen, Richardson, Sidney, Zhang, Qiping, Scassellati, Brian, Vázquez, Marynel
Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit communicative signals from users to understand how they are being perceived during interactions. For example, these signals can be gaze patterns, facial expressi
Externí odkaz:
http://arxiv.org/abs/2402.00190
Autor:
Zhang, Qiping, Tsoi, Nathan, Nagib, Mofeed, Choi, Booyeon, Tan, Jie, Chiang, Hao-Tien Lewis, Vázquez, Marynel
Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we investigate the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and mac
Externí odkaz:
http://arxiv.org/abs/2310.11590
Autor:
Francis, Anthony, Pérez-D'Arpino, Claudia, Li, Chengshu, Xia, Fei, Alahi, Alexandre, Alami, Rachid, Bera, Aniket, Biswas, Abhijat, Biswas, Joydeep, Chandra, Rohan, Chiang, Hao-Tien Lewis, Everett, Michael, Ha, Sehoon, Hart, Justin, How, Jonathan P., Karnan, Haresh, Lee, Tsang-Wei Edward, Manso, Luis J., Mirksy, Reuth, Pirk, Sören, Singamaneni, Phani Teja, Stone, Peter, Taylor, Ada V., Trautman, Peter, Tsoi, Nathan, Vázquez, Marynel, Xiao, Xuesu, Xu, Peng, Yokoyama, Naoki, Toshev, Alexander, Martín-Martín, Roberto
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algori
Externí odkaz:
http://arxiv.org/abs/2306.16740
Deploying interactive systems in-the-wild requires adaptability to situations not encountered in lab environments. Our work details our experience about the impact of architecture choice on behavior reusability and reactivity while deploying a public
Externí odkaz:
http://arxiv.org/abs/2302.00191
Evaluation of social robot navigation inherently requires human input due to its qualitative nature. Motivated by the need to scale human evaluation, we propose a general method for deploying interactive, rich-client robotic simulations on the web. P
Externí odkaz:
http://arxiv.org/abs/2012.12336
Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. G
Externí odkaz:
http://arxiv.org/abs/2012.05292
Social navigation research is performed on a variety of robotic platforms, scenarios, and environments. Making comparisons between navigation algorithms is challenging because of the effort involved in building these systems and the diversity of plat
Externí odkaz:
http://arxiv.org/abs/2009.04300