Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Richard, Antoine"'
Autor:
Batista, Luis F W, Ro, Junghwan, Richard, Antoine, Schroepfer, Pete, Hutchinson, Seth, Pradalier, Cedric
Despite the increasing adoption of Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), there still remain challenges limiting real-world deployment. In this paper, we first integrate buoyancy and hydrodynamics models into a mode
Externí odkaz:
http://arxiv.org/abs/2407.08263
Autor:
Chovet, Loïck Pierre, Garcia, Gabriel Manuel, Bera, Abhishek, Richard, Antoine, Yoshida, Kazuya, Olivares-Mendez, Miguel Angel
Recent advancements in Multi-Robot Systems (MRS) and mesh network technologies pave the way for innovative approaches to explore extreme environments. The Artemis Accords, a series of international agreements, have further catalyzed this progress by
Externí odkaz:
http://arxiv.org/abs/2407.03091
Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have reli
Externí odkaz:
http://arxiv.org/abs/2405.20104
In robotics, motion capture systems have been widely used to measure the accuracy of localization algorithms. Moreover, this infrastructure can also be used for other computer vision tasks, such as the evaluation of Visual (-Inertial) SLAM dynamic in
Externí odkaz:
http://arxiv.org/abs/2403.00976
A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks. By incorporating a limited set of experimental room impulse responses as training data, this approach combines neural networ
Externí odkaz:
http://arxiv.org/abs/2401.01206
Autor:
Barad, Kuldeep R, Orsula, Andrej, Richard, Antoine, Dentler, Jan, Olivares-Mendez, Miguel, Martinez, Carol
Vision-based grasping of unknown objects in unstructured environments is a key challenge for autonomous robotic manipulation. A practical grasp synthesis system is required to generate a diverse set of 6-DoF grasps from which a task-relevant grasp ca
Externí odkaz:
http://arxiv.org/abs/2312.11243
Nowadays, realistic simulation environments are essential to validate and build reliable robotic solutions. This is particularly true when using Reinforcement Learning (RL) based control policies. To this end, both robotics and RL developers need too
Externí odkaz:
http://arxiv.org/abs/2310.07393
Autor:
El-Hariry, Matteo, Richard, Antoine, Muralidharan, Vivek, Yalcin, Baris Can, Geist, Matthieu, Olivares-Mendez, Miguel
This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate microgravity environments on Earth.
Externí odkaz:
http://arxiv.org/abs/2310.04266
Autor:
Song, Junlin, Sanchez-Cuevas, Pedro J., Richard, Antoine, Rajan, Raj Thilak, Olivares-Mendez, Miguel
Accurate global localization is crucial for autonomous navigation and planning. To this end, various GPS-aided Visual-Inertial Odometry (GPS-VIO) fusion algorithms are proposed in the literature. This paper presents a novel GPS-VIO system that is abl
Externí odkaz:
http://arxiv.org/abs/2309.12005
Autor:
Richard, Antoine, Kamohara, Junnosuke, Uno, Kentaro, Santra, Shreya, van der Meer, Dave, Olivares-Mendez, Miguel, Yoshida, Kazuya
Developing algorithms for extra-terrestrial robotic exploration has always been challenging. Along with the complexity associated with these environments, one of the main issues remains the evaluation of said algorithms. With the regained interest in
Externí odkaz:
http://arxiv.org/abs/2309.08997