Zobrazeno 1 - 10
of 2 070
pro vyhledávání: '"Lykov, A"'
Autor:
Guo, Ziang, Lin, Xinhao, Yagudin, Zakhar, Lykov, Artem, Wang, Yong, Li, Yanqiang, Tsetserukou, Dzmitry
Multi-modal end-to-end autonomous driving has shown promising advancements in recent work. By embedding more modalities into end-to-end networks, the system's understanding of both static and dynamic aspects of the driving environment is enhanced, th
Externí odkaz:
http://arxiv.org/abs/2409.12667
Autor:
Lykov, Artem, Cabrera, Miguel Altamirano, Konenkov, Mikhail, Serpiva, Valerii, Gbagbe, Koffivi Fid`ele, Alabbas, Ali, Fedoseev, Aleksey, Moreno, Luis, Khan, Muhammad Haris, Guo, Ziang, Tsetserukou, Dzmitry
This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By le
Externí odkaz:
http://arxiv.org/abs/2409.10106
Autor:
Scheerer, Jan Luca, Lykov, Anton, Kayali, Moe, Fountalis, Ilias, Olteanu, Dan, Vasiloglou, Nikolaos, Suciu, Dan
We demonstrate QirK, a system for answering natural language questions on Knowledge Graphs (KG). QirK can answer structurally complex questions that are still beyond the reach of emerging Large Language Models (LLMs). It does so using a unique combin
Externí odkaz:
http://arxiv.org/abs/2408.07494
Autor:
DeCross, Matthew, Haghshenas, Reza, Liu, Minzhao, Rinaldi, Enrico, Gray, Johnnie, Alexeev, Yuri, Baldwin, Charles H., Bartolotta, John P., Bohn, Matthew, Chertkov, Eli, Cline, Julia, Colina, Jonhas, DelVento, Davide, Dreiling, Joan M., Foltz, Cameron, Gaebler, John P., Gatterman, Thomas M., Gilbreth, Christopher N., Giles, Joshua, Gresh, Dan, Hall, Alex, Hankin, Aaron, Hansen, Azure, Hewitt, Nathan, Hoffman, Ian, Holliman, Craig, Hutson, Ross B., Jacobs, Trent, Johansen, Jacob, Lee, Patricia J., Lehman, Elliot, Lucchetti, Dominic, Lykov, Danylo, Madjarov, Ivaylo S., Mathewson, Brian, Mayer, Karl, Mills, Michael, Niroula, Pradeep, Pino, Juan M., Roman, Conrad, Schecter, Michael, Siegfried, Peter E., Tiemann, Bruce G., Volin, Curtis, Walker, James, Shaydulin, Ruslan, Pistoia, Marco, Moses, Steven. A., Hayes, David, Neyenhuis, Brian, Stutz, Russell P., Foss-Feig, Michael
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized
Externí odkaz:
http://arxiv.org/abs/2406.02501
Camera, LiDAR and radar are common perception sensors for autonomous driving tasks. Robust prediction of 3D object detection is optimally based on the fusion of these sensors. To exploit their abilities wisely remains a challenge because each of thes
Externí odkaz:
http://arxiv.org/abs/2405.11682
The advent of immersive Virtual Reality applications has transformed various domains, yet their integration with advanced artificial intelligence technologies like Visual Language Models remains underexplored. This study introduces a pioneering appro
Externí odkaz:
http://arxiv.org/abs/2405.11537
This paper presents the development of a novel ethical reasoning framework for robots. "Robots Can Feel" is the first system for robots that utilizes a combination of logic and human-like emotion simulation to make decisions in morally complex situat
Externí odkaz:
http://arxiv.org/abs/2405.05824
Autor:
Gbagbe, Koffivi Fidèle, Cabrera, Miguel Altamirano, Alabbas, Ali, Alyunes, Oussama, Lykov, Artem, Tsetserukou, Dzmitry
This research introduces the Bi-VLA (Vision-Language-Action) model, a novel system designed for bimanual robotic dexterous manipulation that seamlessly integrates vision for scene understanding, language comprehension for translating human instructio
Externí odkaz:
http://arxiv.org/abs/2405.06039
Recent research on Large Language Models for autonomous driving shows promise in planning and control. However, high computational demands and hallucinations still challenge accurate trajectory prediction and control signal generation. Deterministic
Externí odkaz:
http://arxiv.org/abs/2405.05885
Autor:
Lykov, Artem, Karaf, Sausar, Martynov, Mikhail, Serpiva, Valerii, Fedoseev, Aleksey, Konenkov, Mikhail, Tsetserukou, Dzmitry
This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of t
Externí odkaz:
http://arxiv.org/abs/2405.05872