Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ameya Pore"'
Autor:
Ameya Pore, Zhen Li, Diego Dall'Alba, Albert Hernansanz, Elena De Momi, Arianna Menciassi, Alicia Casals Gelpí, Jenny Dankelman, Paolo Fiorini, Emmanuel Vander Poorten
Publikováno v:
IEEE Transactions on Robotics. :1-20
Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, inter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::672757ea42f689c06f329d57fa36a1c8
https://doi.org/10.1109/tro.2023.3269384
https://doi.org/10.1109/tro.2023.3269384
Autor:
Luca Marzari, Ameya Pore, Diego Dall'Alba, Gerardo Aragon-Camarasa, Alessandro Farinelli, Paolo Fiorini
Publikováno v:
2021 20th International Conference on Advanced Robotics (ICAR).
Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungry training regime that requires millions of trial and error attempts,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50c91d313553a2c579339994c9891906
https://doi.org/10.1109/icar53236.2021.9659344
https://doi.org/10.1109/icar53236.2021.9659344
Publikováno v:
ICDL
We present an introspective framework inspired by the process of how humans perform introspection. Our working assumption is that neural network activations encode information, and building internal states from these activations can improve the perfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbe0f14b3a58235c2e92c603fee30b8e
https://doi.org/10.1109/icdl49984.2021.9515672
https://doi.org/10.1109/icdl49984.2021.9515672
Autor:
Ameya Pore, Gerardo Aragon-Camarasa
Publikováno v:
ICRA
We present a behaviour-based reinforcement learning approach, inspired by Brook's subsumption architecture, in which simple fully connected networks are trained as reactive behaviours. Our working assumption is that a pick and place robotic task can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c85b89f5720b3778a8707f6613e3d206
https://doi.org/10.1109/icra40945.2020.9197262
https://doi.org/10.1109/icra40945.2020.9197262
Autor:
Marco Piccinelli, Paolo Fiorini, Ameya Pore, Diego Dall'Alba, Enrico Magnabosco, Eleonora Tagliabue
Publikováno v:
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
IROS
IROS
Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f437d5687d4e6152f1ccc83b233ef90
Autor:
Guiqiu Liao, Fernando Gonzalez Herrera, Zhongkai Zhang, Ameya Pore, Luca Sestini, Sujit Kumar Sahu, Oscar Caravaca-Mora, Philippe Zanne, Benoit Rosa, Diego Dall’Alba, Paolo Fiorini, Michel de Mathelin, Florent Nageotte, Michalina J. Gora
Publikováno v:
Clinical Biophotonics II
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c276c24723bd68d7e89c66b790525f3
Autor:
Ameya Pore, Davide Corsi, Enrico Marchesini, Diego Dall'Alba, Alicia Casals, Alessandro Farinelli, Paolo Fiorini
Publikováno v:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This task automation could lead to reduced surgeon's cognitive workload, incre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6ec324c7b7a21463f45b685c22a944b