Interactive Online Learning for Obstacle Classification on a Mobile Robot

Losing V, Hammer B, Wersing H. Interactive Online Learning for Obstacle Classification on a Mobile Robot. Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories