Zobrazeno 1 - 10
of 628
pro vyhledávání: '"Fares J"'
Autor:
Fares J. P. Sayegh, Lionel Mouledous, Catherine Macri, Juliana Pi Macedo, Camille Lejards, Claire Rampon, Laure Verret, Lionel Dahan
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract In most models of neuronal plasticity and memory, dopamine is thought to promote the long-term maintenance of Long-Term Potentiation (LTP) underlying memory processes, but not the initiation of plasticity or new information storage. Here, we
Externí odkaz:
https://doaj.org/article/08d6089b0e66444297531a045683d0dd
Publikováno v:
IEEE Access, Vol 12, Pp 15631-15641 (2024)
One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot’s behaviour safe but also makes it more energy efficient. In this di
Externí odkaz:
https://doaj.org/article/1e0d71a81c084173882f960d0a067b18
Autor:
Naseem Alhousani, Matteo Saveriano, Ibrahim Sevinc, Talha Abdulkuddus, Hatice Kose, Fares J. Abu-Dakka
Publikováno v:
IEEE Access, Vol 11, Pp 111492-111505 (2023)
Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Eucl
Externí odkaz:
https://doaj.org/article/f0c812f1435241d7aa037cd169b74efd
Publikováno v:
IEEE Access, Vol 10, Pp 114143-114152 (2022)
In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian data in robotics include stiffness (symmetric and positive definite matri
Externí odkaz:
https://doaj.org/article/8951c3c63eed442fb4e5c231b17c51f5
Autor:
Rafael J. Escarabajal, Fares J. Abu-Dakka, José L. Pulloquinga, Vicente Mata, Marina Vallés, Ángel Valera
Publikováno v:
Multidisciplinary Journal for Education, Social and Technological Sciences, Vol 7, Iss 2, Pp 30-44 (2020)
The design of rehabilitation exercises applied to sprained ankles requires extreme caution, regarding the trajectories and the speed of the movements that will affect the patient. This paper presents a technique that allows a 3-PRS parallel robot to
Externí odkaz:
https://doaj.org/article/cf71acc8887d437da3c72d683c6ad603
Autor:
Kruzliak, Andrej, Hartvich, Jiri, Patni, Shubhan P., Rustler, Lukas, Behrens, Jan Kristof, Abu-Dakka, Fares J., Mikolajczyk, Krystian, Kyrki, Ville, Hoffmann, Matej
This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory ac
Externí odkaz:
http://arxiv.org/abs/2404.07344
Autor:
Hu, Yingbai, Abu-Dakka, Fares J., Chen, Fei, Luo, Xiao, Li, Zheng, Knoll, Alois, Ding, Weiping
Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL
Externí odkaz:
http://arxiv.org/abs/2403.19916
In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhib
Externí odkaz:
http://arxiv.org/abs/2403.05447
Autor:
Fares J. Abu-Dakka, Matteo Saveriano
Publikováno v:
Frontiers in Robotics and AI, Vol 7 (2020)
Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approac
Externí odkaz:
https://doaj.org/article/3d1f196f925e4b7e9be57bb53180f175