Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lahr, Gustavo J. G."'
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 6, pp. 5631-5638, June 2024
Reinforcement learning (RL) has emerged as a promising paradigm in complex and continuous robotic tasks, however, safe exploration has been one of the main challenges, especially in contact-rich manipulation tasks in unstructured environments. Focusi
Externí odkaz:
http://arxiv.org/abs/2406.13744
Autor:
Zhao, Jianzhuang, Lahr, Gustavo J. G., Tassi, Francesco, Santopaolo, Alessandro, De Momi, Elena, Ajoudani, Arash
This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the con
Externí odkaz:
http://arxiv.org/abs/2209.12563
Autor:
Godoy, Ricardo V., Reis, Tharik J. S., Polegato, Paulo H., Lahr, Gustavo J. G., Saute, Ricardo L., Nakano, Frederico N., Machado, Helio R., Sakamoto, Americo C., Becker, Marcelo, Caurin, Glauco A. P.
Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy.
Externí odkaz:
http://arxiv.org/abs/2209.11172
Autor:
Lahr, Gustavo J. G., Garcia, Henrique B., Ajoudani, Arash, Boaventura, Thiago, Caurin, Glauco A. P.
The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine le
Externí odkaz:
http://arxiv.org/abs/2203.00458
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Zhao, Jianzhuang, Lahr, Gustavo J. G., Tassi, Francesco, Santopaolo, Alessandro, De Momi, Elena, Ajoudani, Arash
This paper proposes a hybrid optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the conta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c48920f4e9900ae2fbf22b8122fa246a
http://arxiv.org/abs/2209.12563
http://arxiv.org/abs/2209.12563