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pro vyhledávání: '"Laezza, Rita"'
Reinforcement learning (RL) shows promise in control problems, but its practical application is often hindered by the complexity arising from intricate reward functions with constraints. While the reward hypothesis suggests these competing demands ca
Externí odkaz:
http://arxiv.org/abs/2410.16790
Autor:
Laezza, Rita, Shetab-Bushehri, Mohammadreza, Waltersson, Gabriel Arslan, Özgür, Erol, Mezouar, Youcef, Karayiannidis, Yiannis
Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. Whil
Externí odkaz:
http://arxiv.org/abs/2403.10290
Humans are able to manipulate Deformable Linear Objects (DLOs) such as cables and wires, with little or no visual information, relying mostly on force sensing. In this work, we propose a reduced DLO model which enables such blind manipulation by keep
Externí odkaz:
http://arxiv.org/abs/2310.06424
Autor:
Laezza, Rita, Karayiannidis, Yiannis
Publikováno v:
2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 4438-4444
Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable
Externí odkaz:
http://arxiv.org/abs/2208.02067