Zobrazeno 1 - 10
of 360
pro vyhledávání: '"Shim, David"'
Autor:
Lee, Seungwook, Azhari, Maulana Bisyir, Kang, Gyuree, Günes, Ozan, Han, Donghun, Shim, David Hyunchul
We present an integrated UAV-hexapod robotic system designed for GNSS-denied maritime operations, capable of autonomous deployment and retrieval of a hexapod robot via a winch mechanism installed on a UAV. This system is intended to address the chall
Externí odkaz:
http://arxiv.org/abs/2410.09606
In real-world field operations, aerial grasping systems face significant challenges in dynamic environments due to strong winds, shifting surfaces, and the need to handle heavy loads. Particularly when dealing with heavy objects, the powerful propell
Externí odkaz:
http://arxiv.org/abs/2409.16181
Autor:
Seong, Hyunki, Shim, David Hyunchul
This paper focuses on the acquisition of mapless navigation skills within unknown environments. We introduce the Skill Q-Network (SQN), a novel reinforcement learning method featuring an adaptive skill ensemble mechanism. Unlike existing methods, our
Externí odkaz:
http://arxiv.org/abs/2403.16664
Autor:
Seong, Hyunki, Shim, David Hyunchul
We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided contrastive loss function, MoNet efficiently learns task-specific decisio
Externí odkaz:
http://arxiv.org/abs/2403.18947
Autor:
Seong, Hyunki, Shim, David Hyunchul
Dogfighting is a challenging scenario in aerial applications that requires a comprehensive understanding of both strategic maneuvers and the aerodynamics of agile aircraft. The aerial agent needs to not only understand tactically evolving maneuvers o
Externí odkaz:
http://arxiv.org/abs/2308.03257
Autor:
Jung, Chanyoung, Finazzi, Andrea, Seong, Hyunki, Lee, Daegyu, Lee, Seungwook, Kim, Bosung, Gang, Gyuri, Han, Seungil, Shim, David Hyunchul
While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is
Externí odkaz:
http://arxiv.org/abs/2303.09463
Publikováno v:
IEEE Control Systems Letters, vol. 7, pp. 1652-1657, 2023
In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization m
Externí odkaz:
http://arxiv.org/abs/2301.01470
Publikováno v:
In Robotics and Autonomous Systems October 2024 180
Autor:
Jung, Sunggoo, Shim, David Hyunchul
This study presents a new methodology for learning-based motion planning for autonomous exploration using aerial robots. Through the reinforcement learning method of learning through trial and error, the action policy is derived that can guide autono
Externí odkaz:
http://arxiv.org/abs/2110.01747