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Incremental Learning of Affordances using Markov Logic Networks

Autor: Potter, George, Burghouts, Gertjan, Sijs, Joris

Publikováno v: IEEE IRC 2024

Affordances enable robots to have a semantic understanding of their surroundings. This allows them to have more acting flexibility when completing a given task. Capturing object affordances in a machine learning model is a difficult task, because of

Externí odkaz: http://arxiv.org/abs/2410.17624

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Open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols

Autor: Burghouts, Gertjan, Hillerström, Fieke, Walraven, Erwin, van Bekkum, Michael, Ruis, Frank, Sijs, Joris, van Mil, Jelle, Dijk, Judith

Publikováno v: International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2024

We consider the problem of finding spatial configurations of multiple objects in images, e.g., a mobile inspection robot is tasked to localize abandoned tools on the floor. We define the spatial configuration of objects by first-order logic in terms

Externí odkaz: http://arxiv.org/abs/2407.13382

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Distributed Bayesian: a continuous Distributed Constraint Optimization Problem solver

Autor: Fransman, Jeroen, Sijs, Joris, Dol, Henry, Theunissen, Erik, De Schutter, Bart

In this work, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the classical DCOP framework towar

Externí odkaz: http://arxiv.org/abs/2002.03252

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