Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Rus, Daniela L."'
Autor:
Baykal, Cenk, Rus, Daniela L
Publikováno v:
arXiv
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::7d39eb319792f3f32422ef55217b89d6
https://hdl.handle.net/1721.1/130461
https://hdl.handle.net/1721.1/130461
Publikováno v:
Tim Seyde
Deep exploration requires coordinated long-term planning. We present a model-based reinforcement learning algorithm that guides policy learning through a value function that exhibits optimism in the face of uncertainty. We capture uncertainty over va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::0705c447c403dc7eea90f8eaeb895b24
https://hdl.handle.net/1721.1/125161
https://hdl.handle.net/1721.1/125161
Autor:
Amini, Alexander, Araki, Brandon, Rus, Daniela, Schwarting, Wilko, Rosman, Guy, Karaman, Sertac, Rus, Daniela L
Publikováno v:
Amini
This paper introduces a new method for end-to-end training of deep neural networks (DNNs) and evaluates it in the context of autonomous driving. DNN training has been shown to result in high accuracy for perception to action learning given sufficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::359eb47b338b4c54635095d6f726cfcd
https://orcid.org/0000-0002-9334-1706
https://orcid.org/0000-0002-9334-1706
Autor:
Chin, Lillian T., Lipton, Jeffrey I, MacCurdy, Robert, Romanishin, John W, Sharma, Chetan, Rus, Daniela L
Publikováno v:
Chin
In this paper, we explore a new class of electric motor-driven compliant actuators based on handed shearing auxetic cylinders. This technique combines the benefits of compliant bodies from soft robotic actuators with the simplicity of direct coupling
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::80b3e3ef6f2a4ddbee7531bce1c48d94
https://orcid.org/0000-0002-1726-151X
https://orcid.org/0000-0002-1726-151X
Akademický článek
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Autor:
Tani, Jacopo, Paull, Liam, Zuber, Maria, Rus, Daniela L, How, Jonathan P, Leonard, John J, Censi, Andrea
Publikováno v:
MIT Web Domain
Teaching robotics is challenging because it is a multidisciplinary, rapidly evolving and experimental discipline that integrates cutting-edge hardware and software. This paper describes the course design and first implementation of Duckietown, a vehi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::fe8119ce414c10a6c63d82cd3025f5f7
https://orcid.org/0000-0003-2652-8017
https://orcid.org/0000-0003-2652-8017
Autor:
Detweiler, Carrick, Banerjee, Sreeja, Jiang, Mingshun, Peri, Francesco, Chen, Robert F., Chen, Robert, Doniec, Marek Wojciech, Rus, Daniela L
Publikováno v:
Multidisciplinary Digital Publishing Institute
Journal of Sensor and Actuator Networks; Volume 3; Issue 2; Pages: 113-149
Journal of Sensor and Actuator Networks, Vol 3, Iss 2, Pp 113-149 (2014)
Journal of Sensor and Actuator Networks; Volume 3; Issue 2; Pages: 113-149
Journal of Sensor and Actuator Networks, Vol 3, Iss 2, Pp 113-149 (2014)
Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradient-based decentralized controller that dynamically adjusts the de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e8217ed0dccfad533b3876448f5f993
https://doi.org/10.3390/jsan3020113
https://doi.org/10.3390/jsan3020113
Autor:
Ward, Thomas M., Hashimoto, Daniel A., Ban, Yutong, Rattner, David W., Inoue, Haruhiro, Lillemoe, Keith D., Rus, Daniela L., Rosman, Guy, Meireles, Ozanan R.
Publikováno v:
Surgical Endoscopy & Other Interventional Techniques; Jul2021, Vol. 35 Issue 7, p4008-4015, 8p
Autor:
DeCastro, Jonathan, Liebenwein, Lucas, Vasile, Cristian-Ioan, Tedrake, Russell L, Karaman, Sertac, Rus, Daniela L
Publikováno v:
Prof. Rus
Ensuring the safety of autonomous vehicles is paramount for their successful deployment. However, formally verifying autonomous driving decisions systems is difficult. In this paper, we propose a frame-work for constructing a set of safety contracts
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::b38a4fabe659c63b782eff1d2468b1b7
https://hdl.handle.net/1721.1/123848
https://hdl.handle.net/1721.1/123848
Akademický článek
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