Zobrazeno 1 - 10
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pro vyhledávání: '"WARD, JAMES P."'
Autor:
Webb, Nicola, Milivojevic, Sanja, Sobhani, Mehdi, Madin, Zachary R., Ward, James C., Yusuf, Sagir, Baber, Chris, Hunt, Edmund R.
For humans and robots to form an effective human-robot team (HRT) there must be sufficient trust between team members throughout a mission. We analyze data from an HRT experiment focused on trust dynamics in teams of one human and two robots, where t
Externí odkaz:
http://arxiv.org/abs/2409.20218
Metric magnitude is a measure of the "size" of point clouds with many desirable geometric properties. It has been adapted to various mathematical contexts and recent work suggests that it can enhance machine learning and optimization algorithms. But
Externí odkaz:
http://arxiv.org/abs/2409.04411
Autor:
Milivojevic, Sanja, Sobhani, Mehdi, Webb, Nicola, Madin, Zachary, Ward, James, Yusuf, Sagir, Baber, Chris, Hunt, Edmund R.
Publikováno v:
TAS 2024: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems
Integrating robots into teams of humans is anticipated to bring significant capability improvements for tasks such as searching potentially hazardous buildings. Trust between humans and robots is recognized as a key enabler for human-robot teaming (H
Externí odkaz:
http://arxiv.org/abs/2408.09531
Autor:
Bhardwaj, Kartikeya, Ward, James, Tung, Caleb, Gope, Dibakar, Meng, Lingchuan, Fedorov, Igor, Chalfin, Alex, Whatmough, Paul, Loh, Danny
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called Restructurable Activation Networks (RANs) that manipulate the amount o
Externí odkaz:
http://arxiv.org/abs/2208.08562
As radio-frequency (RF) antenna, component and processing capabilities increase, the ability to perform multiple RF system functions from a common aperture is being realized. Conducting both radar and communications from the same system is potentiall
Externí odkaz:
http://arxiv.org/abs/2203.09571
Autonomous systems are highly vulnerable to a variety of adversarial attacks on Deep Neural Networks (DNNs). Training-free model-agnostic defenses have recently gained popularity due to their speed, ease of deployment, and ability to work across many
Externí odkaz:
http://arxiv.org/abs/2112.14340
To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high-level contextual understanding of the surroundings is necessary to plan and execute accurate driving maneu
Externí odkaz:
http://arxiv.org/abs/2003.01871
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