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pro vyhledávání: '"Cowan, Noah"'
We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium poi
Externí odkaz:
http://arxiv.org/abs/2411.06612
Robotic adaptation to unanticipated operating conditions is crucial to achieving persistence and robustness in complex real world settings. For a wide range of cutting-edge robotic systems, such as micro- and nano-scale robots, soft robots, medical r
Externí odkaz:
http://arxiv.org/abs/2310.02141
Autor:
Deng, Siming, Liu, Junning, Datta, Bibekananda, Pantula, Aishwarya, Gracias, David H., Nguyen, Thao D., Bittner, Brian A., Cowan, Noah J.
It is challenging to perform system identification on soft robots due to their underactuated, high-dimensional dynamics. In this work, we present a data-driven modeling framework, based on geometric mechanics (also known as gauge theory) that can be
Externí odkaz:
http://arxiv.org/abs/2307.01062
Given a plant subject to delayed sensor measurement, there are several approaches to compensate for the delay. An obvious approach is to address this problem in state space, where the $n$-dimensional plant state is augmented by an $N$-dimensional (Pa
Externí odkaz:
http://arxiv.org/abs/2210.12123
For a general class of translationally invariant systems with a specific category of nonlinearity in the output, this paper presents necessary and sufficient conditions for global observability. Critically, this class of systems cannot be stabilized
Externí odkaz:
http://arxiv.org/abs/2210.03848
Autor:
De Silva, Ashwin, Ramesh, Rahul, Ungar, Lyle, Shuler, Marshall Hussain, Cowan, Noah J., Platt, Michael, Li, Chen, Isik, Leyla, Roh, Seung-Eon, Charles, Adam, Venkataraman, Archana, Caffo, Brian, How, Javier J., Kebschull, Justus M, Krakauer, John W., Bichuch, Maxim, Kinfu, Kaleab Alemayehu, Yezerets, Eva, Jayaraman, Dinesh, Shin, Jong M., Villar, Soledad, Phillips, Ian, Priebe, Carey E., Hartung, Thomas, Miller, Michael I., Dey, Jayanta, Ningyuan, Huang, Eaton, Eric, Etienne-Cummings, Ralph, Ogburn, Elizabeth L., Burns, Randal, Osuagwu, Onyema, Mensh, Brett, Muotri, Alysson R., Brown, Julia, White, Chris, Yang, Weiwei, Rusu, Andrei A., Verstynen, Timothy, Kording, Konrad P., Chaudhari, Pratik, Vogelstein, Joshua T.
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribut
Externí odkaz:
http://arxiv.org/abs/2201.07372
Publikováno v:
In Current Biology 20 May 2024 34(10):2118-2131
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Akademický článek
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Autor:
Madhav, Manu S., Jayakumar, Ravikrishnan P., Lashkari, Shahin G., Savelli, Francesco, Blair, Hugh T., Knierim, James J., Cowan, Noah J.
Publikováno v:
In Journal of Neuroscience Methods 15 February 2022 368