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.

An important goal for research on service robots is the cooperation of a human and a robot as team. A service robot in a domestic environment needs to build a representation of its future workspace that corresponds to the human user's understanding of these surroundings. But it also needs to apply this model about the "where" and "what" in its current interaction to allow communication about objects and places in a human-adequate way. In this paper we present the integration of a hierarchical robotic mapping system into an interactive framework controlled by a dialog system. The goal is to use interactively acquired environment models to implement a robot with interaction aware behaviors. A major contribution of this work is a three-level hierarchy of spatial representation affecting three different communication dimensions. This hierarchy is consequently applied in the design of the grounding-based dialog, laser-based topological mapping, and an objects attention system. We demonstrate the benefits of this integration for learning and tour guiding in a human- comprehensible interaction between a robot and its user in a home-tour scenario. The enhanced interaction capabilities are crucial for developing a new generation of robots that will be accepted not only as service robots but also as robot companions.

A very important aspect in developing robots capable of human-robot interaction (HRI) is the research in natural, human-like communication, and subsequently, the development of a research platform with multiple HRI capabilities for evaluation. Besides a flexible dialog system and speech understanding, an anthropomorphic appearance has the potential to support intuitive usage and understanding of a robot, e.g., human-like facial expressions and deictic gestures can as well be produced and also understood by the robot. As a consequence of our effort in creating an anthropomorphic appearance and to come close to a human- human interaction model for a robot, we decided to use human-like sensors, i.e., two cameras and two microphones only, in analogy to human perceptual capabilities too. Despite the challenges resulting from these limits with respect to perception, a robust attention system for tracking and interacting with multiple persons simultaneously in real time is presented. The tracking approach is sufficiently generic to work on robots with varying hardware, as long as stereo audio data and images of a video camera are available. To easily implement different interaction capabilities like deictic gestures, natural adaptive dialogs, and emotion awareness on the robot, we apply a modular integration approach utilizing XML-based data exchange. The paper focuses on our efforts to bring together different interaction concepts and perception capabilities integrated on a humanoid robot to achieve comprehending human-oriented interaction.

A very important aspect in developing robots capable of human-robot interaction (HRI) is natural, human-like communication. Besides a flexible dialog system and speech understanding an anthropomorphic appearance has many advantages for intuitive usage and understanding of a robot. As a consequence of our effort in creating an anthropomorphic appearance and to come as close as possible to a human-human interaction, we decided to use human-like sensors, i.e., two cameras and two microphones only, not using a laser range finder or omnidirectional camera for tracking persons. Despite the challenge of a limited field of perception, a robust attention system for tracking and interacting with multiple persons simultaneously in real-time was created. Our approach is sufficiently generic to work on robots with varying hardware, as long as stereo audio data and images of a video camera are available. Since the architecture is designed modular with a XML based data exchange we are able to extend the robot's abilities easily

An ambitious goal in modern robotic science is to build mobile robots that are able to interact as companions in real world environments. Especially for caretaking of elderly people a system robustly working at private homes is essential, requiring a very natural and human oriented way of communication. Since home environments are usually very individual a first task for a newly acquired robot is to get familiar with its new environment. This paper gives a short overview on how we integrated a vision based localization using the advantages of a very modular architecture and extending a spoken dialog system for online labeling and interaction about different locations. We present results from the integrated system working in a real, fully furnished home environment where it was able to learn the names of different rooms. This system enables us to perform real user studies in future without the need to fall back to Wizard-of-Oz experiments. Ongoing work aims at enabling the robot to take initiative by asking for unknown locations. A future extension is the ability to generalize over features of known rooms to make predictions when encountering unknown rooms

Creating robots with extendable social skills and interaction capabilities that suffice their operation in the real world with naive users is a very challenging task. In this paper we present a new approach using topic tracking on multi-modal dialogue to provide a mobile robot with a higher level situation awareness in human-robot interaction. The robot is no longer operating in laboratory surroundings, but in its own real world flat. We describe how our topic tracking approach is implemented in this integrated system, operating on verbal speech input. Different modalities like data from video cameras and laser scans are used as additional cues to a semantic understanding and grouping of user utterances into different topics. Both the amount of topics and the according topic names are created dynamically. Evaluating an offline speech corpus demonstrates the suitability of our approach. It is now possible to ask "BIRON, what's the topic?", making the interaction more social