A paradigm shift is taking place in the field of men- tal healthcare and patient wellbeing. Traditionally, the attempts at sustaining and enhancing wellbeing were mainly based on the comparison of the individual with the population average. Recently, attention has shifted towards a more personal, idiographic approach. Such shift calls for new solutions to get data about individu- als, create personalized models of wellbeing and trans- lating these into personalized advice. Idiographic research can be conducted on a large scale by letting people measure themselves. Repeated collec- tion of data, for example by means of questionnaires, provides individuals feedback on and insight into their wellbeing. A way to partially automate this feedback process is by creating software that statistically ana- lyzes, using a method known as vector autoregression, repetitive questionnaire data to determine cause-effect relationships between the measured features. In this pa- per we describe a means to facilitate these repetitive measurements and to partially automate the feedback process. The paper provides an overview and technical description of such automated analyses software, named Autovar, and its use in an online self-measurement plat- form.

A robotic system for extracting and imitating gestures in Human-Robot Interaction is presented. Using the iCub robot and RGB-D sensors, contextually important time points are used to mark and ground the gestures. The benefits of this approach are that the gesture imitation appears both visually and behaviourally realistic, the cognitive and computation loads are reduced and no predefined mappings or speech-based annotations are needed. The system is adaptive to individual users and contributes to the creation and maintenance of a smoother interaction.

The ability to make decisions that balance conflicting needs and variable-quality inputs is a skill that is inherently human. In emergency situations, such capabilities are tested under pressure, as needs and inputs change---often rapidly---and deliberation must take place quickly or else opportunities are lost. This short paper identifies challenges faced when emergency services personnel are supported by human/multi-robot systems. Several strategies are proposed to address these challenges, with deployment geared toward emergency services agencies within the next 5-10 years.

We are looking at new ways of building algorithms for synthesizing and rendering animation in social robots that can keep them as interactive as necessary, while still following on principles and practices used by professional animators. We will be studying the animation process side by side with professional animators in order to understand how these algorithms and tools can be used by animators to achieve animation capable of correctly adapting to the environment and the artificial intelligence that controls the robot. Figure 1: Two example scenarios featuring a touch-based Robotic characters are becoming widespread as useful multimedia application, sensors, and different robots.

The EMPOWER project creates opportunities for physically disabled individuals (namely quadriplegics) to operate robots through the web browser. Robotic technology has the ability to unlock productivity and grant greater purpose to mentally capable, but physically disabled, users. The goal of the EMPOWER project is to foster independence for the physically disabled by lowering barriers such as accessibility and cost. The potential of the EMPOWER project can best be illustrated through the prototyping projects between Mr. Evans and Brown University. Through our web-enabled AR.Drone (running ROS), Mr. Evans has been able to engage in activities in Providence, RI from his home in California, where he remotely pilots AR.Drones. The live video feed from the embedded cameras in the quadricopter provide Mr. Evans a vehicle to explore and interact with people and places far beyond the confines of his bed.

Vocalics is the study of the nonverbal aspects of speech, such as volume, pitch, and rate. Our contribution is a parametric We present an overview of the control, recognition, decision-making, vocalic behavior controller that autonomously adjusts and learning techniques utilized by the Interaction the robot speaker volume based on models of how a Lab (robotics.usc.edu/interaction) at the University human user will hear speech produced by the robot. These of Southern California (USC) to enable autonomy in sociable models vary with distance, orientation, and perceived environmental and socially assistive robots. These techniques are implemented interference (Mead & Matarić 2014). Our future with two software libraries: 1) the Social Behavior work will investigate adapting the pitch and rate of speech Library (SBL) provides autonomous social behavior produced by a robot to improve user speech perception.

A study of long-term interaction with the robot embodiment of the companion called Sarah was conducted during the summer of 2012. The aim of the study was to see long-term implications when the robot embodiment was in a natural setting. The robot interacted with 5 participants for 3 weeks in a office environment running continuously.

Over the last five years, and while developing an architecture for autonomous service robots in human environments, we have identified several key decisional issues that are to be tackled for a cognitive robot to share space and tasks with a human. We introduce some of them here: situation assessment and mutual modelling, management and exploitation of each agent (human and robot) knowledge in separate cognitive models, natural multi-modal communication, "human-aware" task planning, and human and robot interleaved plan achievement. As a general "take home" message, it appears that explicit knowledge management, both symbolic and geometric, proves to be a successful key while attempting to address these challenges, as it pushes for a different, more semantic way to address the decision-making issue in human-robot interactions.

Touch can be a powerful means of communication especially when it is combined with other sensing modalities, such as speech. The challenge on a humanoid robot is to sense touch in a way that can be sensitive to subtle cues, such as the hand used and amount of force applied. We propose a novel combination of sensing modalities to extract touch information. We extract hand information using the Leap Motion active sensor, then determine force information from force sensitive resistors. We combine these sensing modalities at the feature level, then train a support vector machine to recognize specific touch gestures. We demonstrate a high level of accuracy recognizing four different touch gestures from the firefighting domain.

These Intentions are derived from Desires, which represent the satisfaction or inhibition of Intentions For assistive robots, interacting efficiently with humans as generated by Motivations. Just like Behaviors, require the integration of multiple perception and action Motivations are distributed processes from which a decision modalities. For instance, the use of a wide range of sensors can emerge at the Organization Layer. The Intention can easily overload the computing resources of an autonomous Workspace, situated at the Coordination Layer, gathers all robot. Components such as articulated facial expressions Desires to infer the Intentions of the robot: this module determines can provide non-vocal, meaningful communication which specific modules in the Behavioral Layer channels, with no guarantee however that they will be must be activated based on the Desires, by using a set of perceived as natural or pleasant by all of its users in assistive strategies that are related to the robot's capabilities.