Search Results for author: Abhishek Thakur

Found 4 papers, 2 papers with code

NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge

1 code implementation21 Mar 2021 Ali Agha, Kyohei Otsu, Benjamin Morrell, David D. Fan, Rohan Thakker, Angel Santamaria-Navarro, Sung-Kyun Kim, Amanda Bouman, Xianmei Lei, Jeffrey Edlund, Muhammad Fadhil Ginting, Kamak Ebadi, Matthew Anderson, Torkom Pailevanian, Edward Terry, Michael Wolf, Andrea Tagliabue, Tiago Stegun Vaquero, Matteo Palieri, Scott Tepsuporn, Yun Chang, Arash Kalantari, Fernando Chavez, Brett Lopez, Nobuhiro Funabiki, Gregory Miles, Thomas Touma, Alessandro Buscicchio, Jesus Tordesillas, Nikhilesh Alatur, Jeremy Nash, William Walsh, Sunggoo Jung, Hanseob Lee, Christoforos Kanellakis, John Mayo, Scott Harper, Marcel Kaufmann, Anushri Dixit, Gustavo Correa, Carlyn Lee, Jay Gao, Gene Merewether, Jairo Maldonado-Contreras, Gautam Salhotra, Maira Saboia Da Silva, Benjamin Ramtoula, Yuki Kubo, Seyed Fakoorian, Alexander Hatteland, Taeyeon Kim, Tara Bartlett, Alex Stephens, Leon Kim, Chuck Bergh, Eric Heiden, Thomas Lew, Abhishek Cauligi, Tristan Heywood, Andrew Kramer, Henry A. Leopold, Chris Choi, Shreyansh Daftry, Olivier Toupet, Inhwan Wee, Abhishek Thakur, Micah Feras, Giovanni Beltrame, George Nikolakopoulos, David Shim, Luca Carlone, Joel Burdick

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.

Decision Making Motion Planning

AutoCompete: A Framework for Machine Learning Competitions

no code implementations ICML 2015 2015 Abhishek Thakur, Artus Krohn-Grimberghe

In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions.

AutoML

AutoCompete: A Framework for Machine Learning Competition

no code implementations8 Jul 2015 Abhishek Thakur, Artus Krohn-Grimberghe

In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions.

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