Institut des Systèmes Intelligents et de Robotique

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in  he relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.

As the physical limits of Moore's law are being reached, a research effort is launched to achieve further performance improvements by exploring computation paradigms departing from standard approaches. The BAMBI project (Bottom-up Approaches to Machines dedicated to Bayesian Inference) aims at developing hardware dedicated to probabilistic computation, which extends logic computation realised by boolean gates in current computer chips. Such probabilistic computing devices would allow to solve faster and at a lower energy cost a wide range of Artificial Intelligence applications, especially when decisions need to be taken from incomplete data in an uncertain environment. This paper describes an architecture where very simple operators compute on a time coding of probability values as stochastic signals. Simulation tests and a reconfigurable logic hardware implementation demonstrated the feasibility and performances of the proposed inference machine. Hardware results show this architecture can quickly solve Bayesian sensor fusion problems and is very efficient in terms of energy consumption.

The accessibility of infovis authoring tools to a wide audience has been identified as one of the major research challenges. A key task of the authoring process is the development of visual mappings. While the infovis community has long been deeply interested in finding effective visual mappings, comparatively little attention has been placed on how people construct visual mappings. In this paper we present the results of a study designed to shed light on how people spontaneously transform data into visual representations. We asked people to create, update and explain their own information visualizations using simple materials such as tangible building blocks. We learned that all participants, most of whom had no experience in visualization, were readily able to create and talk about their own visualizations. On the basis of our observations, we discuss the actions of our participants in the context of the development of their visual representations and their analytic activities. From this we suggest implications for tool design that can enable broader support for infovis authoring. 
More infos on: http://constructive.gforge.inria.fr

Physical visualizations come in increasingly diverse forms, and are used in domains including art and entertainment, business analytics, and scientific research. However, creating physical visualizations requires laborious craftsmanship and demands expertise in both data visualization and digital fabrication. We present three case studies that illustrate limitations of current visualization fabrication workflows. We then present MakerVis, a prototype tool that integrates