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Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations.
Learning Generative Image Object Manipulations from Language Instructions
The use of adequate feature representations is essential for achieving high performance in high-level human cognitive tasks in computational modeling.
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human.
