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A deep probabilistic framework for heterogeneous self-supervised learning of affordances.

A. Dehban, L. Jamone, A. Kampff, and J. Santos-Victor. Humanoids, page 476-483. IEEE, (2017)

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Learning Deep Features for Robotic Inference From Physical Interactions.A. Dehban, S. Zhang, N. Cauli, L. Jamone, and J. Santos-Victor. IEEE Trans. Cogn. Dev. Syst., 15 (3): 985-999 (September 2023)The Impact of Domain Randomization on Object Detection: A Case Study on Parametric Shapes and Synthetic Textures*.A. Dehban, J. Borrego, R. Figueiredo, P. Moreno, A. Bernardino, and J. Santos-Victor. IROS, page 2593-2600. IEEE, (2019)Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study.M. Nunes, A. Dehban, P. Moreno, and J. Santos-Victor. ICRA, page 8316-8322. IEEE, (2020)Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study.M. Nunes, A. Dehban, P. Moreno, and J. Santos-Victor. CoRR, (2019)Denoising auto-encoders for learning of objects and tools affordances in continuous space.A. Dehban, L. Jamone, A. Kampff, and J. Santos-Victor. ICRA, page 4866-4871. IEEE, (2016)A robust and efficient framework for fast cylinder detection.R. Figueiredo, A. Dehban, P. Moreno, A. Bernardino, J. Santos-Victor, and H. Araújo. Robotics Auton. Syst., (2019)SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics.J. Almeida, P. Schydlo, A. Dehban, and J. Santos-Victor. IROS, page 9569-9576. IEEE, (2021)A generic visual perception domain randomisation framework for Gazebo.J. Borrego, R. Figueiredo, A. Dehban, P. Moreno, A. Bernardino, and J. Santos-Victor. ICARSC, page 237-242. IEEE, (2018)Shape-based attention for identification and localization of cylindrical objects.R. Figueiredo, A. Dehban, A. Bernardino, J. Santos-Victor, and H. Araújo. ICDL-EPIROB, page 98-103. IEEE, (2017)Efficient Resource Allocation for Sparse Multiple Object Tracking.R. Figueiredo, J. Avelino, A. Dehban, A. Bernardino, P. Lima, and H. Araújo. VISIGRAPP (6: VISAPP), page 300-307. SciTePress, (2017)

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A deep probabilistic framework for heterogeneous self-supervised learning of affordances.

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Disambiguation of "Dehban, Atabak"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A deep probabilistic framework for heterogeneous self-supervised learning of affordances.

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A deep probabilistic framework for heterogeneous self-supervised learning of affordances.