Abstract — This work proposes an initial memory model for a long-term artificial companion, which migrates among virtual and robot platforms based on the context of interactions with the human user. This memory model enables the companion to remember events that are relevant or significant to itself or to the user. For other events which are either ethically sensitive or with a lower long-term value, the memory model supports forgetting through the processes of generalisation and memory restructuring. The proposed memory model draws inspiration from the human short-term and long-term memories. The short-term memory will support companions in focusing on the stimuli that are relevant to their current active goals within the environment. The long-term memory will contain episodic events that are chronologically sequenced and derived from the companion’s interaction history both with the environment and the user. There are two key questions that we try to address in this work: 1) What information should the companion remember in order to generate appropriate behaviours and thus smooth the interaction with the user? And, 2) What are the relevant aspects to take into consideration during the design of memory for a companion that can have different types of virtual and physical bodies? Finally, we show an implementation plan of the memory model, focusing on issues of information grounding, activation and sensing based on specific hardware platforms. I.

Several machine learning techniques are used to model the behavior of children with autism interacting with a humanoid robot, comparing a static model to a dynamic model using hand-coded features. Good accuracy (over 80%) is achieved in predicting child vocalizations; directions for future approaches to modeling the behavior of children with autism are suggested. Categories and Subject Descriptors H.1.2 [Models and Principles]:

This article provides a comprehensive introduction to the design of the minimally expressive robot KASPAR which is particularly suitable for human-robot interaction studies. A low-cost design with off-the-shelf components has been used in a novel design inspired from a multi-disciplinary viewpoint, including comics design and Japanese Noh theatre. The design rationale of the robot and its technical features are described in detail. Three research studies will be presented that have been using KASPAR extensively. Firstly, we present its application in robot-assisted play and therapy for children with autism. Secondly, we illustrate its use in human-robot interaction studies investigating the role of interaction kinesics and gestures. Lastly, we describe a study in the field of developmental robotics into computational architectures based on interaction histories for robot ontogeny. The three areas differ in the way how the robot is being operated and its role in social interaction scenarios. Each will be introduced briefly and examples of the results are presented. Reflections on the specific design features of KASPAR that were important in these studies and lessons learnt from these studies concerning the design of

Sensing and interpreting the user’s activities and social behavior, monitoring the dynamics of the social context, and selecting and producing appropriate robot action are the core challenges involved in using robots as social tools in interaction scenarios. In social human-robot interaction, speech and gesture are the commonly considered interaction modalities. In human-human interactions, interpersonal distance between people can contain significant social and communicative information. Thus, if human-robot interaction reflects this human-human interaction property, then human-robot distances also convey social information. If a robot is to be an effective social agent, its actions, including those relating to interpersonal distance, must be appropriate for the given social situation. This becomes a greater challenge in playful and unstructured interactions, such as those involving children. This paper demonstrates the use of a distance-based model for the recognition and expression of spatial social behavior. The model was designed to classify averse social behavior of a child engaged in playful interaction with a robot and uses distance-based features to autonomously identify interaction/play, avoidance, wall-hugging, and parent-proximity behavior with 94 % accuracy. The same methodology was used to model the spatial aspects of a person following a robot and use the model as part of a modified navigation planner to enable the robot to exhibit socially-aware goal-oriented navigation behavior. The model-based planner resulted in robot navigation behavior that was more effective at allowing a partner to follow the robot. This effect was demonstrated using quantitative measures of navigation performance and observer rating of the robot’s behavior. These two uses of spatial models were implemented on complete robot systems and validated in evaluation studies with children with autism spectrum disorders and with neurotypical adults.