Uncertainty Resolution in Misinformation Detection
Yury Orlovskiy
Camille Thibault
Anne Imouza
Kellin Pelrine
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
Firat Oncel
Matthias Bethge
Beyza Ermis
cCaugatay Yildiz
An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments
Benjamin Morrell
Kyohei Otsu
Ali Agha
David D. Fan
Sung-Kyun Kim
Muhammad Fadhil Ginting
Xianmei Lei
Jeffrey Edlund
Seyed Fakoorian
Amanda Bouman
Fernando Chavez
Taeyeon Kim
Gustavo J. Correa
Maira Saboia
Angel Santamaria-Navarro
Brett Lopez
Boseong Kim
Chanyoung Jung
Mamoru Sobue
Oriana Claudia Peltzer … (voir 69 de plus)
Joshua Ott
Robert Trybula
Thomas Touma
Marcel Kaufmann
Tiago Stegun Vaquero
Torkom Pailevanian
Matteo Palieri
Yun Chang
Andrzej Reinke
Matthew Anderson
Frederik E.T. Schöller
Patrick Spieler
Lillian Clark
Avak Archanian
Kenny Chen
Hovhannes Melikyan
Anushri Dixit
Harrison Delecki
Daniel Pastor
Barry Ridge
Nicolas Marchal
Jose Uribe
Sharmita Dey
Kamak Ebadi
Kyle Coble
Alexander Nikitas Dimopoulos
Vivek Thangavelu
Vivek Shankar Vardharajan
Nicholas Palomo
Antoni Rosinol
Arghya Chatterjee
Christoforos Kanellakis
Bjorn Lindqvist
Micah Corah
Kyle Strickland
Ryan Stonebraker
Michael Milano
Christopher E. Denniston
Sami Sahnoune
Thomas Claudet
Seungwook Lee
Gautam Salhotra
Edward Terry
Rithvik Musuku
Robin Schmid
Tony Tran
Ara Kourchians
Justin Schachter
Hector Azpurua
Levi Resende
Arash Kalantari
Jeremy Nash
Josh Lee
Christopher Patterson
Jen Blank
Kartik Patath
Yuki Kubo
Ryan Alimo
Yasin Almalioglu
Aaron Curtis
Jacqueline Sly
Tesla Wells
Nhut T. Ho
Mykel Kochenderfer
George Nikolakopoulos
David Shim
Luca Carlone
Joel Burdick
An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments
Benjamin Morrell
Kyohei Otsu
Ali Agha
David D. Fan
Sung-Kyun Kim
Muhammad Fadhil Ginting
Xianmei Lei
Jeffrey Edlund
Seyed Fakoorian
Amanda Bouman
Fernando Chavez
Taeyeon Kim
Gustavo J. Correa
Maira Saboia
Angel Santamaria-Navarro
Brett Lopez
Boseong Kim
Chanyoung Jung
Mamoru Sobue
Oriana Claudia Peltzer … (voir 69 de plus)
Joshua Ott
Robert Trybula
Thomas Touma
Marcel Kaufmann
Tiago Stegun Vaquero
Torkom Pailevanian
Matteo Palieri
Yun Chang
Andrzej Reinke
Matthew Anderson
Frederik E.T. Schöller
Patrick Spieler
Lillian Clark
Avak Archanian
Kenny Chen
Hovhannes Melikyan
Anushri Dixit
Harrison Delecki
Daniel Pastor
Barry Ridge
Nicolas Marchal
Jose Uribe
Sharmita Dey
Kamak Ebadi
Kyle Coble
Alexander Nikitas Dimopoulos
Vivek Thangavelu
Vivek Shankar Vardharajan
Nicholas Palomo
Antoni Rosinol
Arghya Chatterjee
Christoforos Kanellakis
Bjorn Lindqvist
Micah Corah
Kyle Strickland
Ryan Stonebraker
Michael Milano
Christopher E. Denniston
Sami Sahnoune
Thomas Claudet
Seungwook Lee
Gautam Salhotra
Edward Terry
Rithvik Musuku
Robin Schmid
Tony Tran
Ara Kourchians
Justin Schachter
Hector Azpurua
Levi Resende
Arash Kalantari
Jeremy Nash
Josh Lee
Christopher Patterson
Jen Blank
Kartik Patath
Yuki Kubo
Ryan Alimo
Yasin Almalioglu
Aaron Curtis
Jacqueline Sly
Tesla Wells
Nhut T. Ho
Mykel Kochenderfer
George Nikolakopoulos
David Shim
Luca Carlone
Joel Burdick
This article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robot… (voir plus)s (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal ground–aerial exploration solutions.We demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.
An Addendum to NeBula: Towards Extending TEAM CoSTAR’s Solution to Larger Scale Environments
Benjamin Morrell
Kyohei Otsu
Ali Agha
David D. Fan
Sung-Kyun Kim
Muhammad Fadhil Ginting
Xianmei Lei
Jeffrey Edlund
Seyed Fakoorian
Amanda Bouman
Fernando Chavez
Taeyeon Kim
Gustavo J. Correa
Maira Saboia
Angel Santamaria-Navarro
Brett Lopez
Boseong Kim
Chanyoung Jung
Mamoru Sobue
Oriana Claudia Peltzer … (voir 69 de plus)
Joshua Ott
Robert Trybula
Thomas Touma
Marcel Kaufmann
Tiago Stegun Vaquero
Torkom Pailevanian
Matteo Palieri
Yun Chang
Andrzej Reinke
Matthew Anderson
Frederik E.T. Schöller
Patrick Spieler
Lillian Clark
Avak Archanian
Kenny Chen
Hovhannes Melikyan
Anushri Dixit
Harrison Delecki
Daniel Pastor
Barry Ridge
Nicolas Marchal
Jose Uribe
Sharmita Dey
Kamak Ebadi
Kyle Coble
Alexander Nikitas Dimopoulos
Vivek Thangavelu
Vivek Shankar Vardharajan
Nicholas Palomo
Antoni Rosinol
Arghya Chatterjee
Christoforos Kanellakis
Bjorn Lindqvist
Micah Corah
Kyle Strickland
Ryan Stonebraker
Michael Milano
Christopher E. Denniston
Sami Sahnoune
Thomas Claudet
Seungwook Lee
Gautam Salhotra
Edward Terry
Rithvik Musuku
Robin Schmid
Tony Tran
Ara Kourchians
Justin Schachter
Hector Azpurua
Levi Resende
Arash Kalantari
Jeremy Nash
Josh Lee
Christopher Patterson
Jen Blank
Kartik Patath
Yuki Kubo
Ryan Alimo
Yasin Almalioglu
Aaron Curtis
Jacqueline Sly
Tesla Wells
Nhut T. Ho
Mykel Kochenderfer
George Nikolakopoulos
David Shim
Luca Carlone
Joel Burdick
This paper presents an appendix to the original NeBula autonomy solution [Agha et al., 2021] developed by the TEAM CoSTAR (Collaborative Sub… (voir plus)Terranean Autonomous Robots), participating in the DARPA Subterranean Challenge. Specifically, this paper presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: (i) large-scale geometric and semantic environment mapping; (ii) an adaptive positioning system; (iii) probabilistic traversability analysis and local planning; (iv) large-scale POMDPbased global motion planning and exploration behavior; (v) large-scale networking and decentralized reasoning; (vi) communication-aware mission planning; and (vii) multi-modal ground-aerial exploration solutions. We demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.
Addressing Sample Inefficiency in Multi-View Representation Learning
Arna Ghosh
Kumar Krishna Agrawal
Shagun Sodhani
Affirmative Safety: An Approach to Risk Management for Advanced Ai
Akash Wasil
Joshua Clymer
Emily Dardaman
Simeon Campos
Evan Murphy
AIoT Smart Home via Autonomous LLM Agents
Dmitriy Rivkin
Francois Hogan
Amal Feriani
Abhisek Konar
Adam Sigal
AmbieGen at the SBFT 2024 Tool Competition - CPS-UAV Track
Dmytro Humeniuk
AmbieGenVAE at the SBFT 2024 Tool Competition - Cyber-Physical Systems Track
Dmytro Humeniuk
An Analysis of Quantile Temporal-Difference Learning
Mark Rowland
Remi Munos
Mohammad Gheshlaghi Azar
Yunhao Tang
Georg Ostrovski
Anna Harutyunyan
K. Tuyls
Will Dabney
We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key compon… (voir plus)ent in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Unlike classical TD learning, which can be analysed with standard stochastic approximation tools, QTD updates do not approximate contraction mappings, are highly non-linear, and may have multiple fixed points. The core result of this paper is a proof of convergence to the fixed points of a related family of dynamic programming procedures with probability 1, putting QTD on firm theoretical footing. The proof establishes connections between QTD and non-linear differential inclusions through stochastic approximation theory and non-smooth analysis.
An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants
Meryam Chafiq
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado