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pro vyhledávání: '"Angelov, Daniel"'
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural
Externí odkaz:
http://arxiv.org/abs/2006.11300
Autor:
Burke, Michael, Lu, Katie, Angelov, Daniel, Straižys, Artūras, Innes, Craig, Subr, Kartic, Ramamoorthy, Subramanian
Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal state is known. This work considers the inverse problem, where the goa
Externí odkaz:
http://arxiv.org/abs/2002.01240
Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learning from demonstration depends crucially on inductive bias in the chosen
Externí odkaz:
http://arxiv.org/abs/1907.13627
Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics. These change-points are often exploited in hierarchical motion planning to build approximate models
Externí odkaz:
http://arxiv.org/abs/1907.08199
Autor:
Asenov, Martin, Burke, Michael, Angelov, Daniel, Davchev, Todor, Subr, Kartic, Ramamoorthy, Subramanian
Videos provide a rich source of information, but it is generally hard to extract dynamical parameters of interest. Inferring those parameters from a video stream would be beneficial for physical reasoning. Robots performing tasks in dynamic environme
Externí odkaz:
http://arxiv.org/abs/1907.06422
Many realistic robotics tasks are best solved compositionally, through control architectures that sequentially invoke primitives and achieve error correction through the use of loops and conditionals taking the system back to alternative earlier stat
Externí odkaz:
http://arxiv.org/abs/1906.10099
Publikováno v:
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Pages 1341-1349, Montreal QC, Canada, May 13 - 17, 2019
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work we show that it is possible to learn a generative model for distinct user behavioral
Externí odkaz:
http://arxiv.org/abs/1903.01267
Akademický článek
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Autor:
Nenkov, Veselin, Angelov, Daniel
Publikováno v:
Математика и информатика / Mathematics and Informatics. 60(1):64-80
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=585530
Akademický článek
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