Semi-supervised learning of semantic spatial concepts for a mobile robot

The ability of building robust semantic space representations of environments is crucial for the development of truly autonomous robots. This task, inherently connected with cognition, is traditionally achieved by training the robot with a supervised learning phase. This work argues that the design of robust and autonomous systems would greatly benefit from adopting a semi-supervised online learning approach. Indeed, the support of open-ended, lifelong learning is fundamental in order to cope with the dazzling variability of the real world, and online learning provides precisely this kind of ability. The research focused on the robot place recognition problem, and designed online categorization algorithms that occasionally ask for human intervention based on a confidence measure. By modulating the number of queries on the experienced data sequence, the system adaptively trades off performance with degree of supervision. Through a rigorous analysis, simultaneous performance and query rate guarantees are proven in extremely robust (game-theoretic) data generation scenarios. This theoretical analysis is supported and complemented by extensive evaluation on data acquired on real robotic platforms.