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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.