This paper examines young children's beliefs and preconceived notions about robots. Robots have several features (e.g., boundary-like features, human-like features, and room for imagination) that may elicit social responses and trigger serious thinking in children. An in-depth interview was conducted with 77 children between the ages 4- to 7-years old to examine how they perceive and understand robots. The findings revealed the type of prior knowledge and beliefs children revert to, and how age influenced how they see and interpret robots. The findings may assist researchers when designing human-robot interaction with young children.

This proxemics study examines whether the physical distance between robots and humans differ based on the following factors: 1) age: children vs. adults, 2) who initiates the approach: humans approaching the robot vs. robot approaching humans, 3) prompting: verbal invitation vs. non-verbal gesture (e.g., beckoning), and 4) informing: announcement vs. permission vs. nothing. Results showed that both verbal and non-verbal prompting had significant influence on physical distance. Physiological data is also used to detect the appropriate timing of approach for a more natural and comfortable interaction.

Two studies examined the different features of humanoid robots and the influence on children's affective behavior. The first study looked at interaction styles and general features of robots. The second study looked at how the robot's attention influences children's behavior and engagement. Through activities familiar to young children (e.g., table setting, story telling), the first study found that cooperative interaction style elicited more oculesic behavior and social engagement. The second study found that quality of attention, type of attention, and length of interaction influences affective behavior and engagement. In the quality of attention, Wizard-of-Oz (woz) elicited the most affective behavior, but automatic attention worked as well as woz when the interaction was short. The type of attention going from nonverbal to verbal attention increased children's oculesic behavior, utterance, and physiological response. Affective interactions did not seem to depend on a single mechanism, but a well-chosen confluence of technical features.

We present a model that is capable of synchronizing expressive gestures with speech. The model, implemented on a Honda humanoid robot, can generate a full range of gesture types, such as emblems, iconic and metaphoric gestures, deictic pointing and beat gestures. Arbitrary input text is analyzed with a part-of-speech tagger and a text-to-speech engine for timing information of spoken words. In addition, style tags can be optionally added to specify the level of excitement or topic changes. The text, combined with any tags, is then processed by several grammars, one for each gesture type to produce several candidate gestures for each word of the text. The model then selects probabilistically amongst the gesture types based on the desired degree of expressivity. Once a gesture type is selected, it coincides with a particular gesture template, consisting of trajectory curves that define the gesture. Speech timing patterns and style parameters are used to modulate the shape of the curve before it sent to the whole body control system on the robot. Evaluation of the model's parameters were performed, demonstrating the ability of observers to differentiate varying levels of expressiveness, excitement and speech synchronization. Modification of gesture speed for trajectory tracking found that positive associations like happiness and excitement accompanied faster speeds, with negative associations like sadness or tiredness occurred at slower speeds.

Extended human-robot interactions possess unique aspects which are not exhibited in short-term interactions spanning a few minutes or extremely long-term spanning days. In order to comprehensively monitor such interactions, we need special recording mechanisms which ensure the interaction is captured at multiple spatio-temporal scales, viewpoints and modalities(audio, video, physio). To minimize cognitive burden, we need tools which can automate the process of annotating and analyzing the resulting data. In addition, we also require these tools to be able to provide a unified, multi-scale view of the data and help discover patterns in the interaction process. In this paper, we describe recording and analysis tools which are helping us analyze extended human-robot interactions with children as subjects. We also provide some experimental results which highlight the utility of such tools.

Humanoid robots consist of biologically inspired features, human-like appearance, and intelligent behavior that naturally elicit social responses. Complex interactions are now possible, where children interact and learn from robots. A pilot study attempted to determine which features in robots led to changes in learning and behavior. Three common learning styles, lecture, cooperative, and self-directed, were implemented into ASIMO to see if children can learn from robots. General features such as monotone robot-like voice and human-like voice were compared. Thirty-seven children between the ages 4-to 10- years participated in the study. Each child engaged in a table-setting task with ASIMO that exhibited different learning styles and general features. Children answered questions in relation to a table-setting task with a learning measure. Promissory evidence shows that learning styles and general features matter especially for younger children.