We present the design process, realization and evaluation of a robot system for nteractive object learning. The system-oriented evaluation, in particular, addresses an open problem for the evaluation of systems, where overall user satisfaction depends not only on the performance of the parts, but also on their combination, and on user behavior. Based on the PARADISE method known from spoken dialog systems, we have defined and applied internal and external metrics for fine-grained and largely automatable identification of such relationships. Through evaluation with n=28 subjects, indicator functions explaining up to 55% of variation in several satisfaction metrics were found. Furthermore, we demonstrate that the system's interaction style reduces the need for instruction and successfully recovers partial failures.

If robots are to succeed in novel tasks, they must be able to learn from humans. To improve such human-robot interaction, a system is presented that provides dialog structure and engages the human in an exploratory teaching scenario. Thereby, we specifically target untrained users, who are supported by mixed-initiative interaction using verbal and non-verbal modalities. We present the principles of dialog structuring based on an object learning and manipulation scenario. System development is following an interactive evaluation approach and we will present both an extensible, event-based interaction architecture to realize mixed-initiative and evaluation results based on a video-study of the system. We show that users benefit from the provided dialog structure to result in predictable and successful human-robot interaction.

In scenarios that require a close collaboration and knowledge transfer between inexperienced users and robots, the ldquolearning by interactingrdquo paradigm goes hand in hand with appropriate representations and learning methods. In this paper we discuss a mixed initiative strategy for robotic learning by interacting with a user in a joint map acquisition process. We propose the integration of an environment representation approach into our interactive learning framework. The environment representation and mapping system supports both user driven and data driven strategies for the acquisition of spatial information, so that a mixed initiative strategy for the learning process is realised. We evaluate our system with test runs according to the scenario of a guided tour, extending the area of operation from structured laboratory environment to less predictable domestic settings.