Zobrazeno 1 - 10
of 260
pro vyhledávání: '"Castri, P"'
The study of cause-and-effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a maj
Externí odkaz:
http://arxiv.org/abs/2410.02844
Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot i
Externí odkaz:
http://arxiv.org/abs/2407.01593
Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the a
Externí odkaz:
http://arxiv.org/abs/2406.04955
Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects. Modelling cause-and-effect relations through causal inference aids in predicting human behaviours and anticipating robot interventions. Howev
Externí odkaz:
http://arxiv.org/abs/2402.16068
Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects. Specifically, creatin
Externí odkaz:
http://arxiv.org/abs/2310.14925
Deploying service robots in our daily life, whether in restaurants, warehouses or hospitals, calls for the need to reason on the interactions happening in dense and dynamic scenes. In this paper, we present and benchmark three new approaches to model
Externí odkaz:
http://arxiv.org/abs/2307.00065
Autor:
Alexa J. Davis, Donna M. Halperin, Brian R. Condran, Melissa S. Kervin, Antonia M. Di Castri, Katherine L. Salter, Julie A. Bettinger, Janet A. Parsons, Scott A. Halperin
Publikováno v:
BMC Public Health, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background The COVID-19 pandemic and subsequent implementation of public health policies exacerbated multiple intersecting systemic inequities, including homelessness. Housing is a key social determinant of health that played a significant p
Externí odkaz:
https://doaj.org/article/9cdfed642dd64765aebc9e34fa1c8d57
Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human
Externí odkaz:
http://arxiv.org/abs/2304.11740
Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In this paper, we propose an extension of a state-of-the-art causal discov
Externí odkaz:
http://arxiv.org/abs/2302.10135
Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis
Externí odkaz:
http://arxiv.org/abs/2301.03886