In child-robot interaction research, many studies pursue the goal to support children's language development. While research in human-human interaction suggests that changing human partners during children's language learning can reduce their recall performance of the learning content, little is known whether a change in social robots as interaction partners influence children's learning in the same way. In this paper, we present findings from a word learning study, in which we changed the robotic partner for one group of children while the other group interacted with the same robot. Contrary to work with human social partners, we found that children did not retrieve words differently when interacting with different humanoid robots as their social interaction partners.

Social robots hold potential for implementation in future educational environments by engaging children in motivating and embodied learning settings. However, how children enter into and maintain social interactions is substantially influenced by their individual differences. In this paper, we address children’s temperamental trait of shyness and explore how it influences children’s proxemic behavior in a long-term interaction involving language learning with either a social robot or a human. We operationalized proxemics by measuring children’s time spent in four different proxemic zones during the interaction. Overall, our results highlight that shy children approach the interaction partner in a more distant way when compared with less shy peers. When both interaction partners (robot and human) were compared with each other, however, our findings suggest that shy children display similar proxemic behavior. Findings are discussed with regard to the role of shyness during children’s interactions with social robots and the implications for future interaction design.

Technological advancements in machine learning affecting humans' lives on the one hand and also regulatory initiatives fostering transparency in algorithmic decision making on the other hand drive a recent surge of interest in explainable AI (XAI). Explainability is discussed as a solution to sociotechnical challenges such as intelligent software providing incomprehensible decisions or big data enabling fast learning but becoming too complex to fully comprehend and judge its achievements. With explainable AI, more insights into the functions, decisions, and usefulness of algorithms are expected.

Research in human-robot interaction envisions applications in a variety of areas. In one of them, robots can be used to improve educational performance. However, most scientific investigations focus on learning outcomes without considering the social implications of a robot available as a learning companion. Therefore, the identification of the underlying challenges and issues faced by different stakeholders involved in the technology implementation processes is still sparse. This paper is concerned with parents as a key stakeholder group that is almost overlooked in the existing literature. We present results of a study on child-robot interaction for language learning, in which parents accompanied their children and evaluated the robot after four sessions of experiencing it within a laboratory setting. The results suggest that parents recognize the robots' potential for language learning within a playful interaction with their children. However, as parents reported, the technical challenges for an adaptive and smooth interaction might impede children's learning gains in the long-term.

Robots are not human. They might in some cases have a similar appearance but different behavioral and cognitive strengths and limitations. In this sense, an interaction with a robot is asymmetric. When interacting with a robot one is unsure what behavior to expect as the appearance does not necessarily make the abilities of the robot transparent. In human-human interaction, we can also find asymmetric interactions to occur. For example, in an interaction with a child, adults have to adapt to the learner's capabilities and understanding. Similarly, in interactions with special populations such as persons with autistic spectrum disorders (ASD), asymmetry occurs as specific information seems to be processed differently.

We present a novel approach to learn and combine multiple input to output mappings. Our system can employ the mappings to find solutions that satisfy multiple task constraints simultaneously. This is done by training a network for each mapping independently and maintaining all solutions to multivalued mappings. Redundancies are resolved online through dynamic competitions in neural fields. The performance of the approach is demonstrated in the example application of inverse kinematics learning. We show simulation results for the humanoid robot iCub where we trained two networks: One to learn the kinematics of the robot's arm and one to learn which postures are close to joint limits. We show how our approach can be used to easily integrate multiple mappings that have been learned separately from each other. When multiple goals are given to the system, such as reaching for a target location and avoiding joint limits, it dynamically selects a solution that satisfies as many goals as possible.

The paper investigates the effects of a robot's “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 system's perception of the tutor's actions using the idea of reciprocity of actions. Sequential micro-analysis of two opposite cases reveals how the robot's behavior (responsive vs. non-responsive) pro-actively shapes the tutor's 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 user's perception and expectations of the system's competences and lead to a relatively stable tutoring style even if the robot's reactivity and appropriateness of feedback changes.

Robots interacting with humans need to understand actions and make use of language in social interactions. Research on infant development has shown that language helps the learner to structure visual observations of action. This acoustic information typically in the form of narration overlaps with action sequences and provides infants with a bottom-up guide to find structure within them. This concept has been introduced as acoustic packaging by Hirsh-Pasek and Golinkoff. We developed and integrated a prominence detection module in our acoustic packaging system to detect semantically relevant information linguistically\nhighlighted by the tutor. Evaluation results on speech data from adult-infant interactions show a significant agreement with human raters. Furthermore a first approach based on acoustic packages which uses the prominence detection results to generate acoustic feedback is presented.\n\nIndex Terms: prominence, multimodal action segmentation,\nhuman robot interaction, feedback

Gesture is an important feature of social interaction, frequently used by human speakers to illustrate what speech alone cannot provide, e.g. to convey referential, spatial or iconic information. Accordingly, humanoid robots that are intended to engage in natural human-robot interaction should produce speech-accompanying gestures for comprehensible and believable behavior. But how does a robot's non-verbal behavior influence human evaluation of communication quality and the robot itself? To address this research question we conducted two experimental studies. Using the Honda humanoid robot we investigated how humans perceive various gestural patterns performed by the robot as they interact in a situational context. Our findings suggest that the robot is evaluated more positively when non-verbal behaviors such as hand and arm gestures are displayed along with speech. These findings were found to be enhanced when the participants were explicitly requested to direct their attention towards the robot during the interaction.

Recent developments in robotics enable advanced human-robot interaction. Especially interactions of novice users with robots are often unpredictable and, therefore, demand for novel methods for the analysis of the interaction in systemic ways. We propose Systemic Interaction Analysis (SInA) as a method to jointly analyze system level and interaction level in an integrated manner using one tool. The approach allows us to trace back patterns that deviate from prototypical interaction sequences to the distinct system components of our autonomous robot. In this paper, we exemplarily apply the method to the analysis of the follow behavior of our domestic robot BIRON. The analysis is the basis to achieve our goal of improving human-robot interaction iteratively.

This paper investigates the influence of feedback provided by an autonomous robot (BIRON) on users' discursive behavior. A user study is described during which users show objects to the robot. The results of the experiment indicate, that the robot's verbal feedback utterances cause the humans to adapt their own way of speaking. The changes in users' verbal behavior are due to their beliefs about the robots knowledge and abilities. In this paper they are identified and grouped. Moreover, the data implies variations in user behavior regarding gestures. Unlike speech, the robot was not able to give feedback with gestures. Due to the lack of feedback, users did not seem to have a consistent mental representation of the robot's abilities to recognize gestures. As a result, changes between different gestures are interpreted to be unconscious variations accompanying speech.

How to teach actions to a robot as well as how a robot learns actions is an important issue to be discussed in designing robot learning systems. Inspired by human parent-infant interaction, we hypothesize that a robot equipped with infant-like abilities can take advantage of parental proper teaching. Parents are known to significantly alter their infant-directed actions versus adult-directed ones, e.g. make more pauses between movements, which is assumed to aid the infants' understanding of the actions. As a first step, we analyzed parental actions using a primal attention model. The model based on visual saliency can detect likely important locations in a scene without employing any knowledge about the actions or the environment. Our statistical analysis revealed that the model was able to extract meaningful structures of the actions, e.g. the initial and final state of the actions and the significant state changes in them, which were highlighted by parental action modifications. We further discuss the issue of designing an infant-like robot that can induce parent-like teaching, and present a human-robot interaction experiment evaluating our robot simulation equipped with the saliency model.

The paper introduces an online user study on applications for social robots with 127 participants. The potential users proposed 570 application scenarios based on the appearance and functionality of four robots presented (AIBO, BARTHOC, BIRON, iCat). The items were grouped into 13 categories which are interpreted and discussed by means of four dimensions: public vs. private use, intensity of interaction, complexity of interaction model, and functional vs. human-like appearance. The interpretation lead to three classes of applications for social robots according to the degree of social interaction: (1) Specialized Applications where the robot has to perform clearly defined tasks which are delegated by a user, (2) Public Applications which are directed to the communication with many users, and (3) Individual Applications with the need of a highly elaborated social model to maintain a variety of situations with few people.

The capability to coordinate and interrelate speech and vision is a virtual prerequisite for adaptive, cooperative, and flexible interaction among people. It is therefore fair to assume that human-machine interaction, too, would benefit from intelligent interfaces for integrated speech and image processing. We first sketch an interactive system that integrates automatic speech processing with image understanding. Then, we concentrate on performance assessment which we believe is an emerging key issue in multimodal interaction. We explain the benefit of time scale analysis and usability studies and evaluate our system accordingly.