To fulfill the dream of having autonomous robots at home, there is a need for spatial representations augmented with semantic concepts. Vision has emerged recently as the key modality to recognize semantic categories like places (office, corridor, kitchen, etc). A crucial aspect of these semantic place representations is that they change over time, due to the dynamism of the world. This calls for visual algorithms able to learn from experience while at the same time managing the continuous flow of incoming data. This paper addresses these issues by presenting an SVM-based algorithm able to (a) learn continuously from experience with a fast updating rule, and (b) control the memory growth via a random forgetting mechanism while at the same time preserving an accuracy comparable to that of the batch algorithm. We apply our method to two different scenarios where learning from experience plays an important role: (1) continuous learning of visual places under dynamic changes, and (2) knowledge transfer of visual concepts across robot platforms. For both scenarios, results confirm the effectiveness of our approach.
展开▼
