Towards a Robot Architecture for Situated Lifelong Object Learning The ability to acquire knowledge incrementally and after deployment is of utmost importance for robots operating in the real world. Moreover, robots that have to operate alongside people need to be able to interact in a way that is intuitive for the users, e.g., by understanding and producing natural language. In this paper we present a first prototype of a robot architecture developed for situated lifelong object learning. The system is able to communicate with its users through natural language and perform object learning and recognition on the spot through situated interactions. In this first stage, we evaluate the system in terms of recognition accuracy which gives an indirect measure of the quality of the collected data with the proposed pipeline. Our results show that the robot can use this data for both learning and recognition with acceptable incremental performance. We also discuss limitations and steps that are necessary in order to improve performance as well as to shed some light on system usability.

Look Further to Recognize Better: Learning Shared Topics and CategorySpecific Dictionaries for OpenEnded 3D Object Recognition Service robots are expected to operate effectively in humancentric environments for long periods of time. In such realistic scenarios, finegrained object categorization is as important as basiclevel object categorization. We tackle this problem by proposing an openended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and categoryspecific dictionaries. The general topics encode the common patterns of all categories, while the categoryspecific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous stateoftheart approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a realworld setting. Regarding computation time, the best result was obtained with a BagofWords method followed by a variant of the Latent Dirichlet Allocation approach.