Zobrazeno 1 - 10
of 36
pro vyhledávání: '"McCarthy, Zoe"'
Autor:
Puchalski, Adam, McCarthy, Zoë, Palaoro, Alexandre Varaschin, Salamatin, Arthur A., Nagy-Mehesz, Agnes, Korneva, Guzeliya, Beard, Charles E., Owens, Jeffery, Adler, Peter H., Kornev, Konstantin G.
Publikováno v:
In Acta Biomaterialia August 2024 184:273-285
Autor:
Zubov, Kirill, McCarthy, Zoe, Ma, Yingbo, Calisto, Francesco, Pagliarino, Valerio, Azeglio, Simone, Bottero, Luca, Luján, Emmanuel, Sulzer, Valentin, Bharambe, Ashutosh, Vinchhi, Nand, Balakrishnan, Kaushik, Upadhyay, Devesh, Rackauckas, Chris
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical models. In this manuscript we detail the inner workings of NeuralPDE.j
Externí odkaz:
http://arxiv.org/abs/2107.09443
Autor:
Gealy, David V., McKinley, Stephen, Yi, Brent, Wu, Philipp, Downey, Phillip R., Balke, Greg, Zhao, Allan, Guo, Menglong, Thomasson, Rachel, Sinclair, Anthony, Cuellar, Peter, McCarthy, Zoe, Abbeel, Pieter
Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradigm for robotic force-controlled manipulation in human environments at
Externí odkaz:
http://arxiv.org/abs/1904.03815
Autor:
Zhang, Tianhao, McCarthy, Zoe, Jow, Owen, Lee, Dennis, Chen, Xi, Goldberg, Ken, Abbeel, Pieter
Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Real
Externí odkaz:
http://arxiv.org/abs/1710.04615
Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilis
Externí odkaz:
http://arxiv.org/abs/1705.05394
Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning policies with internal memory for high-dimensional, continuous system
Externí odkaz:
http://arxiv.org/abs/1507.01273
Akademický článek
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Autor:
Lawson Jones, Gaynor, York, Helen, Lawal, Olanrewaju, Cherrill, Richard, Mercer, Sarah, McCarthy, Zoe
Publikováno v:
Journal of Medical Radiation Sciences; Dec2021, Vol. 68 Issue 4, p418-425, 8p