Zobrazeno 1 - 10
of 1 365
pro vyhledávání: '"Kolling, P."'
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
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:
Wu, Qinyuan, Khan, Mohammad Aflah, Das, Soumi, Nanda, Vedant, Ghosh, Bishwamittra, Kolling, Camila, Speicher, Till, Bindschaedler, Laurent, Gummadi, Krishna P., Terzi, Evimaria
We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs). We leverage the in-context learning (ICL) abilities of LLMs to estimate the extent to which an LLM knows the facts stored in a knowledge base. Ou
Externí odkaz:
http://arxiv.org/abs/2404.12957
Autor:
de Moraes, Linda Dotto, Kich, Victor Augusto, Kolling, Alisson Henrique, Bottega, Jair Augusto, Grando, Ricardo Bedin, Cukla, Anselmo Rafael, Gamarra, Daniel Fernando Tello
Publikováno v:
20th IEEE Latin American Robotics Symposium - LARS 2023 and 15th Brazilian Symposium on Robotics- SBR 2023
In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. The research methodology primarily involves a comparative analysis betw
Externí odkaz:
http://arxiv.org/abs/2310.13809
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
With the increasing reliance on deep neural networks, it is important to develop ways to better understand their learned representations. Representation similarity measures have emerged as a popular tool for examining learned representations However,
Externí odkaz:
http://arxiv.org/abs/2305.19294
Autor:
de Moraes, Linda Dotto, Kich, Victor Augusto, Kolling, Alisson Henrique, Bottega, Jair Augusto, Steinmetz, Raul, da Silva, Emerson Cassiano, Grando, Ricardo Bedin, Cuckla, Anselmo Rafael, Gamarra, Daniel Fernando Tello
Publikováno v:
International Conference on Intelligent Systems Design and Applications, 2022
In this work, we present two Deep Reinforcement Learning (Deep-RL) approaches to enhance the problem of mapless navigation for a terrestrial mobile robot. Our methodology focus on comparing a Deep-RL technique based on the Deep Q-Network (DQN) algori
Externí odkaz:
http://arxiv.org/abs/2301.11173
Autor:
Jan Grohn, Nima Khalighinejad, Caroline I Jahn, Alessandro Bongioanni, Urs Schüffelgen, Jerome Sallet, Matthew F. S. Rushworth, Nils Kolling
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Staying engaged is necessary to maintain goal-directed behaviors. Despite this, engagement exhibits continuous, intrinsic fluctuations. Even in experimental settings, animals, unlike most humans, repeatedly and spontaneously move between per
Externí odkaz:
https://doaj.org/article/d56ed8e1f59c420c87bafc18e61308a3
Autor:
Brendin B. Flinn, Hayden M. O’Dell, Kara M. Joseph, Amanda L. Smythers, David P. Neff, Leslie M. Hicks, Michael L. Norton, Derrick R. J. Kolling
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Tardigrades are renowned for their ability to survive a wide array of environmental stressors. In particular, tardigrades can curl in on themselves while losing a significant proportion of their internal water content to form a structure ref
Externí odkaz:
https://doaj.org/article/8b21fc1d316a42fc8507db68ef744d0e