A Formal Account of Structuring Motor Actions With Sensory Prediction for a Naive Agent

Autor: Jean-Merwan Godon, Sylvain Argentieri, Bruno Gas

Publikováno v: Frontiers in Robotics and AI, Vol 7 (2020)

For naive robots to become truly autonomous, they need a means of developing their perceptive capabilities instead of relying on hand crafted models. The sensorimotor contingency theory asserts that such a way resides in learning invariants of the se

Externí odkaz: https://doaj.org/article/059ea54d40294426949bbbf3c0aad3b5

Acquisition of a space representation by a naive agent from sensorimotor invariance and proprioceptive compensation

Autor: Gurvan Le Clec’H, Bruno Gas, J. Kevin O’Regan

Publikováno v: International Journal of Advanced Robotic Systems, Vol 13 (2016)

In this article, we present a simple agent which learns an internal representation of space without a priori knowledge of its environment, body, or sensors. The learned environment is seen as an internal space representation. This representation is i

Externí odkaz: https://doaj.org/article/279ede398bff4af9963047fc8544bf79

ECF: A C＋＋ framework for evolutionary computation

Autor: Domagoj Jakobovic, Marko Đurasević, Stjepan Picek, Bruno Gašperov

Publikováno v: SoftwareX, Vol 27, Iss , Pp 101640- (2024)

Metaheuristics have been shown to be efficient techniques for addressing a wide range of complex optimization problems. Developing flexible, reliable, and efficient frameworks for evolutionary computation metaheuristics is of great importance. With t

Externí odkaz: https://doaj.org/article/3c883ba203ca431dafa867639183cd01

Audition as a Trigger of Head Movements

Autor: Bruno Gas, Sylvain Argentieri, Benjamin Cohen-Lhyver

Publikováno v: Modern Acoustics and Signal Processing ISBN: 9783030003852

In multimodal realistic environments, audition and vision are the prominent two sensory modalities that work together to provide humans with a best possible perceptual understanding of the environment. Yet, when designing artificial binaural systems,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1073f99aaa8c66b15e595346ec5cad30
https://hal.archives-ouvertes.fr/hal-02128846/document

Learning an internal representation of the end-effector configuration space

Autor: Alexander V. Terekhov, Bruno Gas, Alban Laflaquière, J. Kevin O'Regan

Publikováno v: IROS
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013,-, Unknown Region. to-appear

Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c516ce732916ccad836c9d576e153cc4
http://arxiv.org/abs/1810.01866