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Social Navigation with Conditional Neural Processes

What is it about?

A data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn the mobile robot's global and local controllers from observations, demonstrating that the proposed framework can successfully carry out navigation tasks regarding social norms in the data.

Why is it important?

Sociability is essential for modern robots to increase their acceptance in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures to learn pedestrian behavior from observations.

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Yigit YILDIRIM
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