Zobrazeno 1 - 10
of 17 713
pro vyhledávání: '"Drews, A."'
Autor:
Kich, Victor Augusto, Kolling, Alisson Henrique, de Jesus, Junior Costa, Heisler, Gabriel V., Jacobs, Hiago, Bottega, Jair Augusto, Kelbouscas, André L. da S., Ohya, Akihisa, Grando, Ricardo Bedin, Drews-Jr, Paulo Lilles Jorge, Gamarra, Daniel Fernando Tello
This paper introduces novel deep reinforcement learning (Deep-RL) techniques using parallel distributional actor-critic networks for navigating terrestrial mobile robots. Our approaches use laser range findings, relative distance, and angle to the ta
Externí odkaz:
http://arxiv.org/abs/2408.05744
Polar ice develops anisotropic crystal orientation fabrics under deformation, yet ice is most often modelled as an isotropic fluid. We present three-dimensional simulations of the crystal orientation fabric of Derwael Ice Rise including the surroundi
Externí odkaz:
http://arxiv.org/abs/2408.01069
Autor:
Grando, Ricardo B., Steinmetz, Raul, Kich, Victor A., Kolling, Alisson H., Furik, Pablo M., de Jesus, Junior C., Guterres, Bruna V., Gamarra, Daniel T., Guerra, Rodrigo S., Drews-Jr, Paulo L. J.
Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhancing motion control and decision-making through a wide range of robotic applications. While prior research has demonstrated the efficacy of DRL algorithms in facilitating a
Externí odkaz:
http://arxiv.org/abs/2406.01952
Autor:
Gosala, Nikhil, Petek, Kürsat, Kiran, B Ravi, Yogamani, Senthil, Drews-Jr, Paulo, Burgard, Wolfram, Valada, Abhinav
Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on l
Externí odkaz:
http://arxiv.org/abs/2405.18852
Autor:
Li, Congqiao, Agapitos, Antonios, Drews, Jovin, Duarte, Javier, Fu, Dawei, Gao, Leyun, Kansal, Raghav, Kasieczka, Gregor, Moureaux, Louis, Qu, Huilin, Suarez, Cristina Mantilla, Li, Qiang
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited t
Externí odkaz:
http://arxiv.org/abs/2405.12972
This study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and Use
Externí odkaz:
http://arxiv.org/abs/2312.08957
Autor:
Moss, Guy, Višnjević, Vjeran, Eisen, Olaf, Oraschewski, Falk M., Schröder, Cornelius, Macke, Jakob H., Drews, Reinhard
The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. The geometry of ice shelves, and hence their buttressing strength, is determined by ice flow as well as by the local surface accumula
Externí odkaz:
http://arxiv.org/abs/2312.02997
Autor:
Drews, Selina, Kohler, Michael
Recent results show that estimates defined by over-parametrized deep neural networks learned by applying gradient descent to a regularized empirical $L_2$ risk are universally consistent and achieve good rates of convergence. In this paper, we show t
Externí odkaz:
http://arxiv.org/abs/2311.14609
Autor:
Kolling, Alisson Henrique, Kich, Victor Augusto, de Jesus, Junior Costa, da Silva, Andressa Cavalcante, Grando, Ricardo Bedin, Drews-Jr, Paulo Lilles Jorge, Gamarra, Daniel F. T.
This work presents a study on parallel and distributional deep reinforcement learning applied to the mapless navigation of UAVs. For this, we developed an approach based on the Soft Actor-Critic method, producing a distributed and distributional vari
Externí odkaz:
http://arxiv.org/abs/2309.00176
Autor:
Grando, Ricardo B., de Jesus, Junior C., Kich, Victor A., Kolling, Alisson H., Guerra, Rodrigo S., Drews-Jr, Paulo L. J.
Deep Reinforcement Learning (Deep-RL) techniques for motion control have been continuously used to deal with decision-making problems for a wide variety of robots. Previous works showed that Deep-RL can be applied to perform mapless navigation, inclu
Externí odkaz:
http://arxiv.org/abs/2308.09811