Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Panerati, Jacopo"'
Autor:
Bosello, Michael, Aguiari, Davide, Keuter, Yvo, Pallotta, Enrico, Kiade, Sara, Caminati, Gyordan, Pinzarrone, Flavio, Halepota, Junaid, Panerati, Jacopo, Pau, Giovanni
Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and eva
Externí odkaz:
http://arxiv.org/abs/2311.02667
Autor:
Teetaert, Spencer, Zhao, Wenda, Xinyuan, Niu, Zahir, Hashir, Leong, Huiyu, Hidalgo, Michel, Puga, Gerardo, Lorente, Tomas, Espinosa, Nahuel, Carrasco, John Alejandro Duarte, Zhang, Kaizheng, Di, Jian, Jin, Tao, Li, Xiaohan, Zhou, Yijia, Liang, Xiuhua, Zhang, Chenxu, Loquercio, Antonio, Zhou, Siqi, Brunke, Lukas, Greeff, Melissa, Hoenig, Wolfgang, Panerati, Jacopo, Schoellig, Angela P.
Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportunity to researchers with different origins, backgrounds, and levels of
Externí odkaz:
http://arxiv.org/abs/2308.16743
Autor:
Zhou, Siqi, Brunke, Lukas, Tao, Allen, Hall, Adam W., Bejarano, Federico Pizarro, Panerati, Jacopo, Schoellig, Angela P.
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science develop more advanced algorithms, we become more dependent on complex com
Externí odkaz:
http://arxiv.org/abs/2308.10008
Autor:
Glossop, Catherine R., Panerati, Jacopo, Krishnan, Amrit, Yuan, Zhaocong, Schoellig, Angela P.
In this study, we leverage the deliberate and systematic fault-injection capabilities of an open-source benchmark suite to perform a series of experiments on state-of-the-art deep and robust reinforcement learning algorithms. We aim to benchmark robu
Externí odkaz:
http://arxiv.org/abs/2210.15199
Autor:
Yuan, Zhaocong, Hall, Adam W., Zhou, Siqi, Brunke, Lukas, Greeff, Melissa, Panerati, Jacopo, Schoellig, Angela P.
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained significant traction. However, to adequately gauge the progress and a
Externí odkaz:
http://arxiv.org/abs/2109.06325
Autor:
Brunke, Lukas, Greeff, Melissa, Hall, Adam W., Yuan, Zhaocong, Zhou, Siqi, Panerati, Jacopo, Schoellig, Angela P.
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of
Externí odkaz:
http://arxiv.org/abs/2108.06266
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cos
Externí odkaz:
http://arxiv.org/abs/2103.01885
Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulations coupled with a problem specification in the form of a reward func
Externí odkaz:
http://arxiv.org/abs/2103.02142
Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promising solution which is low-cost, lightweight, and computationally inex
Externí odkaz:
http://arxiv.org/abs/2003.09371
Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable vehicle interactions. Learning-based methods---such as deep reinforcem
Externí odkaz:
http://arxiv.org/abs/1911.11699