Individuals diagnosed with Autism Spectrum Disorders (ASD) often have challenging behaviors (CB's), such as self-injury or emotional outbursts, which can negatively impact the quality of life of themselves and those around them. Recent advances in mobile and ubiquitous technologies provide an opportunity to efficiently and accurately capture important information preceding and associated with these CB's. The ability to obtain this type of data will help with both intervention and behavioral phenotyping efforts. Through collaboration with behavioral scientists and therapists, we identified relevant design requirements and created an easy-to-use mobile application for collecting, labeling, and sharing in-situ behavior data in individuals diagnosed with ASD. Furthermore, we have released the application to the community as an open-source project so it can be validated and extended by other researchers.

We report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27 % improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments.

Localizing facial features is a critical component in many computer vision applications such as expression recognition, face recognition, face tracking, animation, and red-eye correction. Practical applications require detectors that operate reliably under a wide range of conditions, including variations in illumination, pose, ethnicity, gender and age. One challenge for the development of such detectors is the inherent trade-off between robustness and precision. Robust detectors tend to provide poor localization and detectors sensitive to small changes in local structure, which are needed for precise localization, generate a large number of false alarms. Here we present an approach to this trade-off based on context dependent inference. First, robust detectors are used to detect contexts in which target features occur, then precise detectors are trained to localize the features given the detected context. This paper describes the approach and presents a thorough empirical examination of the parameters needed to achieve practical levels of performance, including the size of the training database, size of the detector's receptive fields and methods for information integration. The approach operates in real time and achieves, to our knowledge, the most accurate localization performance to date.

The goal of the RUBI project is to accelerate progress in the development of social robots by addressing the problem at multiple levels, including the development of a scientific agenda, research methods, formal approaches, software, and hardware. The project is based on the idea that progress will go hand-in-hand with the emergence of a new scientific discipline that focuses on understanding the organization of adaptive behavior in real-time within the environments in which organisms operate. As such, the RUBI project emphasizes the process of design by immersion, i.e., embedding scientists, engineers and robots in everyday life environments so as to have these environments shape the hardware, software, and scientific questions as early as possible in the development process. The focus of the project so far has been on social robots that interact with 18 to 24 month old toddlers as part of their daily activities at the Early Childhood Education Center at the University of California, San Diego. In this document we present an overall assessment of the lessons and progress through year two of the project.