Learning Social Navigation from Demonstrations with Conditional Neural Processes

Understand

Sociability is essential for modern robots to increase their acceptability in human environments.

• Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation.
• However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain.
• This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations.