Zobrazeno 1 - 10
of 594
pro vyhledávání: '"Şeker, M."'
Autor:
Seker, M. Yunus, Kroemer, Oliver
Optimizing robotic action parameters is a significant challenge for manipulation tasks that demand high levels of precision and generalization. Using a model-based approach, the robot must quickly reason about the outcomes of different actions using
Externí odkaz:
http://arxiv.org/abs/2403.11313
Autor:
Seker, M. Yunus, Kroemer, Oliver
Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of ob
Externí odkaz:
http://arxiv.org/abs/2310.11749
Autor:
Ada, Suzan Ece, Seker, M. Yunus
Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adv
Externí odkaz:
http://arxiv.org/abs/2111.14934
Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biolog
Externí odkaz:
http://arxiv.org/abs/2106.08422
We propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning. Our robot interacts with objects using an initial action repertoire that is
Externí odkaz:
http://arxiv.org/abs/2012.02532
Autor:
Akbulut, M. Tuluhan, Oztop, Erhan, Seker, M. Yunus, Xue, Honghu, Tekden, Ahmet E., Ugur, Emre
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). In this paper, we propose a
Externí odkaz:
http://arxiv.org/abs/2003.11334
Akademický článek
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In recent years, graph neural networks have been successfully applied for learning the dynamics of complex and partially observable physical systems. However, their use in the robotics domain is, to date, still limited. In this paper, we introduce Be
Externí odkaz:
http://arxiv.org/abs/1909.03785
Autor:
Doğan, Remziye, Saygı, Mehmet, Birdal, Oğuzhan, Gülcü, Oktay, Güler, Gamze Babur, Şeker, M. Cüneyt, Atae, M.Younus, Güler, Arda, Gökçe, Kaan, Şen, Doğan, Bulut, Muhammed, Yücel, Enver, Özkalaycı, Flora, Karagöz, Ali, Tanboğa, İbrahim Halil
Publikováno v:
Acta Cardiologica; Nov2024, Vol. 79 Issue 9, p995-1003, 9p
Publikováno v:
In Neural Networks February 2022 146:22-35