Karola Pitsch

Revealing Gauguin: engaging visitors in robot guide's explanation in an art museum

Designing technologies that support the explanation of museum exhibits is a challenging domain. In this paper we develop an innovative approach - providing a robot guide with resources to engage visitors in an interaction about an art exhibit. We draw upon ethnographical fieldwork in an art museum, focusing on how tour guides interrelate talk and visual conduct, specifically how they ask questions of different kinds to engage and involve visitors in lengthy explanations of an exhibit. From this analysis we have developed a robot guide that can coordinate its utterances and body movement to monitor the responses of visitors to these. Detailed analysis of the interaction between the robot and visitors in an art museum suggests that such simple devices derived from the study of human interaction might be useful in engaging visitors in explanations of complex artifacts.

Virtual Agents as Daily Assistants for Elderly or Cognitively Impaired People

People with cognitive impairments have problems organizing their daily life autonomously. A virtual agent as daily calendar assistant could provide valuable support, but this requires that these special user groups accept such a system and can interact with it successfully. In this paper we present studies to elucidate these questions for elderly users as well as cognitively impaired users. Results from interviews and focus groups show that acceptance can be increased by way of a participatory design method. Actual interaction studies with a prototype demonstrate the feasibility of spoken-language interaction and reveal strategies to mitigate understanding problems.

Tutor Spotter: Proposing a Feature Set and Evaluating It in a Robotic System

From learning by observation, robotic research has moved towards investigations of learning by interaction. This research is inspired by findings from developmental studies on human children and primates pointing to the fact that learning takes place in a social environment. Recently, driven by the idea that learning through observation or imitation is limited because the observed action not always reveals its meaning, scaffolding or bootstrapping processes supporting learning received increased attention. However, in order to take advantage of teaching strategies, a system needs to be sensitive to a tutor as children are. We therefore developed a module allowing for spotting the tutor by monitoring her or his gaze and detecting modifications in object presentation in form of a looming action. In this article, we will present the current state of the development of our contingency detection system as a set of features.

“The first five seconds”: Contingent stepwise entry into an interaction as a means to secure sustained engagement in HRI

If robot systems are being deployed in real world settings with untrained users who happen to accidentally pass by or could leave at any moment in time, then this places specific demands on the robot system: it needs to secure and maintain the users engagement. In this, a common and critical problem consists of entering into a ‘focused encounter’. It requires each interactional partner to closely react upon the others actions on a very fine-grained level engaging in a stepwise and dynamic process of mutual adjustments. We report initial findings from a study in which we have developed a preliminary, simple solution to this problem inspired by work from Conversation Analysis [7]. Using this as an instrument to explore the impact of a ‘contingent’ (CE) vs. ‘non-contingent entry’ (NCE), we find that users who enter into the interaction in a dynamic and contingent manner show a significantly different way of interacting with the robot than the NCE group.

Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning

Robot learning by imitation requires the detection of a tutors action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutors movement demonstrations in the process of action learning. We argue that the robots feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.

How Can I Help? - Spatial Attention Strategies for a Receptionist Robot

Social interaction between humans takes place in the spatial environment on a daily basis. We occupy space for ourselves and respect the dynamics of spaces that are occupied by others. In human-robot interaction, spatial models are commonly used for structuring relatively far-away interactions or passing-by scenarios. This work instead, focuses on the transition between distant and close communication for an interaction opening. We applied a spatial model to a humanoid robot and implemented an attention system that is connected to it. The resulting behaviors have been verified in an online video study. The questionnaire revealed that these behaviors are applicable and result in a robot that has been perceived as more interested in the human and shows its attention and intentions earlier and to a higher degree than other strategies.

A robot as fitness companion: Towards an interactive action-based motivation model

The topic of motivation is a crucial issue for various human-robot interaction (HRI) scenarios. Interactional aspects of motivation can be studied in human-human interaction (HHI) and build the basis for modeling a robots interactional conduct. Using an ethnographic approach we explored the factors relevant in the formation of motivation-relevant processes in an indoor-cycling activity. We propose an interactive, action-based motivation model for HRI that has been implemented in an autonomous robot system and tested during a long-term HRI study. The model is based on micro-analyses of human indoor cycling courses and resulted in an adaption of specific dialog patterns for HRI. A qualitative evaluation - accompanied by a quantitative analysis - demonstrated that the transfer of interaction patterns from HHI to HRI was successful with participants benefitting from the interaction experience (e.g., performance, subjective feeling of being motivated).

Collaboration in Augmented Reality: How to establish coordination and joint attention?

We present an initial investigation from a semi-experimental setting, in which an HMD-based AR-system has been used for real-time collaboration in a task-oriented scenario (design of a museum exhibition). Analysis points out the specific conditions of interacting in an AR environment and focuses on one particular practical problem for the participants in coordinating their interaction: how to establish joint attention towards the same object or referent. Analysis allows insights into how the pair of users begins to familarize with the environment, the limitations and opportunities of the setting and how they establish new routines for e.g. solving the ‘joint attention’-problem.

Better be reactive at the beginning. Implications of the first seconds of an encounter for the tutoring style in human-robot-interaction

The paper investigates the effects of a robots “on-line” feedback during a tutoring situation with a human tutor. Analysis is based on a study conducted with an iCub robot that autonomously generates its feedback (gaze, pointing gesture) based on the systems perception of the tutors actions using the idea of reciprocity of actions. Sequential micro-analysis of two opposite cases reveals how the robots behavior (responsive vs. non-responsive) pro-actively shapes the tutors conduct and thus co-produces the way in which it is being tutored. A dialogic and a monologic tutoring style are distinguished. The first 20 seconds of an encounter are found to shape the users perception and expectations of the systems competences and lead to a relatively stable tutoring style even if the robots reactivity and appropriateness of feedback changes.

Developing feedback: How children of different age contribute to a tutoring interaction with adults

Learning is a social and interactional endeavor, in which the learner generally receives support from his/her social environment [1]. In this process, the learners feedback is important as it provides information about the learners current understanding which, in turn, enables the tutor to adjust his/her presentation accordingly [2], [3]. Thus, through their feedback learners can actively shape the tutors presentation — a resource which is highly valuable, if we aim at enabling robot systems to learn from a tutor in social interaction. But what kind of feedback should a robot produce and at which time? In this paper, we analyze the interaction between parents and their infants (8 to 30 months) in a tutoring scenario with regard to the feedback provided by the learner in three different age groups. Our combined qualitative and quantitative analysis reveals which features of the feedback change with the infants progressing age and cognitive capabilities.