Bogdan Moldovan

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— Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in(More)
Probabilistic logic programming languages are powerful formalisms that can model complex problems where it is necessary to represent both structure and uncertainty. Using exact inference methods to compute conditional probabilities in these languages is often intractable so approximate inference techniques are necessary. This paper proposes a Markov Chain(More)
We present initial results of an application of statistical re-lational learning using ProbLog to a robotic manipulation task modeled using affordances. Affordances encompass the action possibilities on an object, so previous works have presented models for just one object. However , in scenarios where there are multiple objects that interact between each(More)
— In this paper we employ probabilistic relational affordance models in a robotic manipulation task. Such affor-dance models capture the interdependencies between properties of multiple objects, executed actions, and effects of those actions on objects. Recently it was shown how to learn such models from observed video demonstrations of actions manipulating(More)
The behavior of clusters formed by magnetic particles of magnetic liquid placed into a cylindrical capillary tube in magnetic field is described. Spicular clusters are formed from the sediment at the application of a magnetic field. They arrange themselves along the capillary repeating the direction of external magnetic field. Clusters distribute uniformly(More)
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