Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Miguel A, Fullana"'
Autor:
Miguel Calvo-Fullana, Alejandro Ribeiro, Daniel Mox, Jonathan Fink, Alexander Pyattaev, Vijay Kumar
Publikováno v:
IEEE Robotics and Automation Letters. 6:1120-1127
Multi-agent systems play an important role in modern robotics. Due to the nature of these systems, coordination among agents via communication is frequently necessary. Indeed, Perception-Action-Communication (PAC) loops, or Perception-Action loops cl
Publikováno v:
IEEE Transactions on Signal Processing. 69:5947-5962
Reinforcement learning (RL) is a framework to optimize a control policy using rewards that are revealed by the system as a response to a control action. In its standard form, RL involves a single agent that uses its policy to accomplish a specific ta
Publikováno v:
IEEE Communications Magazine. 59:22-27
Driven by their versatility and autonomy, advanced unmanned aerial systems have indicated substantial promise along the development of current communication networks, as well as in support of new communication platforms. The resulting integration of
Autor:
Miguel Calvo-Fullana, Jonathan P. How
This work presents a distributed estimation algorithm that efficiently uses the available communication resources. The approach is based on Bayesian filtering that is distributed across a network by using the logarithmic opinion pool operator. Commun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b0fab4d1548936a013479c7d5dcd320
Autor:
Patrick A.F. Laing, Bram Vervliet, Christopher G. Davey, Bradford A. Moffat, Ben J. Harrison, Kim L Felmingham, Rebecca Glarin, Trevor Steward, Matthew D. Greaves, Miguel A. Fullana
Safety learning generates associative links between neutral stimuli and the absence of threat, promoting the inhibition of fear and security-seeking behaviours. Precisely how safety learning is mediated at the level of underlying brain systems, parti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6abb567b89bc0bf4ec059846b9ba774d
https://doi.org/10.1101/2021.11.09.467993
https://doi.org/10.1101/2021.11.09.467993
Autor:
Carles Soriano-Mas, Miguel A. Fullana
Publikováno v:
Biological psychiatry. Cognitive neuroscience and neuroimaging. 6(11)
Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::690778176d5c80bd729e2f38b8724a32
http://arxiv.org/abs/2103.05134
http://arxiv.org/abs/2103.05134
Publikováno v:
ACC
Safety is a critical feature of controller design for physical systems. When designing control policies, several approaches to guarantee this aspect of autonomy have been proposed, such as robust controllers or control barrier functions. However, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6072d6ffa15a797fe3ee249fc915f9f4
http://arxiv.org/abs/2102.12585
http://arxiv.org/abs/2102.12585
In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae3f62b9bd281a433dc0e9d00e272ee9
http://arxiv.org/abs/2010.12993
http://arxiv.org/abs/2010.12993
Autor:
Alejandro Ribeiro, Vijay Kumar, Mikhail Gerasimenko, Daniel Mox, Jonathan Fink, Miguel Calvo-Fullana
Publikováno v:
ICRA
In this work, we introduce Mobile Wireless In-frastructure on Demand: a framework for providing wireless connectivity to multi-robot teams via autonomously reconfiguring ad-hoc networks. In many cases, previous multi-agent systems either assumed the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79ff6088a82fcdc16607963ab9ce78b0