Anna-Lisa Vollmer

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.

An Alternative to Mapping a Word onto a Concept in Language Acquisition: Pragmatic Frames

The classic mapping metaphor posits that children learn a word by mapping it onto a concept of an object or event. However, we believe that a mapping metaphor cannot account for word learning, because even though children focus attention on objects, they do not necessarily remember the connection between the word and the referent unless it is framed pragmatically, that is, within a task. Our theoretical paper proposes an alternative mechanism for word learning. Our main premise is that word learning occurs as children accomplish a goal in cooperation with a partner. We follow Bruner’s (1983) idea and further specify pragmatic frames as the learning units that drive language acquisition and cognitive development. These units consist of a sequence of actions and verbal behaviors that are co-constructed with a partner to achieve a joint goal. We elaborate on this alternative, offer some initial parametrizations of the concept, and embed it in current language learning approaches.

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.

Reducing noise and redundancy in registered range data for planar surface extraction

This paper presents a new method for detecting and merging redundant points in registered range data. Given a global representation from sequences of 3D points, the points are projected onto a virtual image plane computed from the intrinsic parameters of the sensor. Candidates for redundancy are collected per pixel which then are clustered locally via region growing and replaced by the clusterpsilas mean value. As data is provided in a certain manner defined by camera characteristics, this processing step preserves the structural information of the data. For evaluation, our approach is compared to two other algorithms. Applied to two different sequences, it is shown that the presented method gives smooth results within planar regions of the point clouds by successfully reducing noise and redundancy and thus improves registered range data.

Tracking gaze over time in HRI as a proxy for engagement and attribution of social agency

In this contribution, we describe a method of analysing and interpreting the direction and timing of a human’s gaze over time towards a robot whilst interacting. Based on annotated video recordings of the interactions, this post-hoc analysis can be used to determine how this gaze behaviour changes over the course of an interaction, following from the observation that humans change their behaviour towards the robot on the time-scale of individual interactions. We posit that given these circumstances, this measure may be used as

Pragmatic Frames for Teaching and Learning in Human-Robot Interaction: Review and Challenges.

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Alignment to the Actions of a Robot

proxy (among others) for engagement in the interaction or the human’s attribution of social agency to the robot. Application of this method to a sample of unstructured childrobot interactions demonstrates its use, and justifies its utilisation in future studies. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems; D.2.8 [Software Engineering]: Metrics— complexity measures, performance measures General Terms Experimentation, Measurement, Theory

Children conform, adults resist: A robot group induced peer pressure on normative social conformity

One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning–teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human–human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching.

Studying the Co-Construction of Interaction Protocols in Collaborative Tasks with Humans

Alignment is a phenomenon observed in human conversation: Dialog partners’ behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human–computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors’ beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version.

Optimal alignments of longest common subsequences and their path properties

Children increasingly yielded to social pressure exerted by a group of robots; however, adults resisted being influenced by our robots. People are known to change their behavior and decisions to conform to others, even for obviously incorrect facts. Because of recent developments in artificial intelligence and robotics, robots are increasingly found in human environments, and there, they form a novel social presence. It is as yet unclear whether and to what extent these social robots are able to exert pressure similar to human peers. This study used the Asch paradigm, which shows how participants conform to others while performing a visual judgment task. We first replicated the finding that adults are influenced by their peers but showed that they resist social pressure from a group of small humanoid robots. Next, we repeated the study with 7- to 9-year-old children and showed that children conform to the robots. This raises opportunities as well as concerns for the use of social robots with young and vulnerable cross-sections of society; although conforming can be beneficial, the potential for misuse and the potential impact of erroneous performance cannot be ignored.