Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Bredeche, Nicolas"'
Autor:
Cazenille, Leo, Lobato-Dauzier, Nicolas, Loi, Alessia, Ito, Mika, Marchal, Olivier, Aubert-Kato, Nathanael, Bredeche, Nicolas, Genot, Anthony J.
Swarm robotics promises adaptability to unknown situations and robustness against failures. However, it still struggles with global tasks that require understanding the broader context in which the robots operate, such as identifying the shape of the
Externí odkaz:
http://arxiv.org/abs/2403.17147
Multi-agent deep reinforcement learning (MADRL) problems often encounter the challenge of sparse rewards. This challenge becomes even more pronounced when coordination among agents is necessary. As performance depends not only on one agent's behavior
Externí odkaz:
http://arxiv.org/abs/2402.03972
Publikováno v:
Physical Review E 110.1 (2024): 014606
We extend the study of the inertial effects on the dynamics of active agents to the case where self-alignment is present. In contrast with the most common models of active particles, we find that self-alignment, which couples the rotational dynamics
Externí odkaz:
http://arxiv.org/abs/2401.17798
Conventional distributed approaches to coverage control may suffer from lack of convergence and poor performance, due to the fact that agents have limited information, especially in non-convex discrete environments. To address this issue, we extend t
Externí odkaz:
http://arxiv.org/abs/2401.03752
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap has mostly been overlooked and the simulated results hardly transfer to
Externí odkaz:
http://arxiv.org/abs/2203.01148
Publikováno v:
Science Robotics 2023, 8,75
Whereas naturally occurring swarms thrive when crowded, physical interactions in robotic swarms are either avoided or carefully controlled, thus limiting their operational density. Here we present a mechanical design rule that allows robots to act in
Externí odkaz:
http://arxiv.org/abs/2111.06953
Publikováno v:
PLoS Comput Biol 18(2): e1009882 (2022)
Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the
Externí odkaz:
http://arxiv.org/abs/2106.10015
This paper focuses on a class of reinforcement learning problems where significant events are rare and limited to a single positive reward per episode. A typical example is that of an agent who has to choose a partner to cooperate with, while a large
Externí odkaz:
http://arxiv.org/abs/2103.06846
Autor:
Doncieux, Stephane, Bredeche, Nicolas, Goff, Léni Le, Girard, Benoît, Coninx, Alexandre, Sigaud, Olivier, Khamassi, Mehdi, Díaz-Rodríguez, Natalia, Filliat, David, Hospedales, Timothy, Eiben, A., Duro, Richard
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate be
Externí odkaz:
http://arxiv.org/abs/2005.06223
Swarms of molecular robots are a promising approach to create specific shapes at the microscopic scale through self-assembly. However, controlling their behavior is a challenging problem as it involves complex non-linear dynamics and high experimenta
Externí odkaz:
http://arxiv.org/abs/1910.00230