Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Darío Urbina-Meléndez"'
Publikováno v:
Frontiers in Robotics and AI, Vol 5 (2018)
In addition to a vestibular system, birds uniquely have a balance-sensing organ within the pelvis, called the lumbosacral organ (LSO). The LSO is well developed in terrestrial birds, possibly to facilitate balance control in perching and terrestrial
Externí odkaz:
https://doaj.org/article/b25d041d6438418fb972807bee650927
Autor:
Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero-Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava Siegelmann
Publikováno v:
Nature Machine Intelligence. 4:196-210
ispartof: NATURE MACHINE INTELLIGENCE vol:4 issue:3 pages:196-210 status: published
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a55395bd1b994789c5a9fc0daeeaebb
https://doi.org/10.1038/s42256-022-00452-0
https://doi.org/10.1038/s42256-022-00452-0
Publikováno v:
Nature machine intelligence
Robots will become ubiquitously useful only when they require just a few attempts to teach themselves to perform different tasks, even with complex bodies and in dynamic environments. Vertebrates use sparse trial and error to learn multiple tasks, de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a25ddce54e2cafb40b6901c899387d91
https://doi.org/10.1038/s42256-019-0029-0
https://doi.org/10.1038/s42256-019-0029-0
Publikováno v:
EMBC
Error feedback is known to improve performance by correcting control signals in response to perturbations. Here we show how adding simple error feedback can also accelerate and robustify autonomous learning in a tendon-driven robot. We have implement
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1dcd2f965681385d30cc81d6d28a5c99
http://arxiv.org/abs/1907.04539
http://arxiv.org/abs/1907.04539
