Subnational policies pertaining to photovoltaic (PV) systems have increased in volume in recent years and federal incentives are set to be phased out over the next few. Understanding how subnational policies function within and across jurisdictions, thereby impacting PV market development, informs policy decision making. This report was developed for subnational policy-makers and researchers in order to aid the analysis on the function of PV system incentives within the emerging PV deployment market. The analysis presented is based on a ‘logic engine’, a database tool using existing state, utility, and local incentives allowing users to see the interrelationships between PV system incentives and parameters, such as geographic location, technology specifications, and financial factors. Depending on how it is queried, the database can yield insights into which combinations of incentives are available and most advantageous to the PV system owner or developer under particular circumstances. This is useful both for individual system developers to identify the most advantageous incentive packages that they qualify for as well as for researchers and policymakers to better understand the patch work of incentives nationwide as well as how they drive the market. In the case of the latter, findings from initial queries identify a limited connection between incentives and market development (based on current data) and point to differing complexities for system developers depending on system owner and size. The entire effort reveals (or possibly reiterates) a critical lack of data on both local policy environments and the structure of market penetration to be able to understand the impact of subnational incentives on the market.

Computer Science researchers have long sought ways to apply the fruits of their labors to education. From the Logo turtles to the latest Cognitive Tutors, the allure of computers that will understand and help humans learn and grow has been a constant thread in Artificial Intelligence research. Now, advances in robotics and our understanding of Human-Robot Interaction make it feasible to develop physically-present robots that are capable of presenting educational material in an engaging manner, adapting online to sensory information from individual students, and building sophisticated, personalized models of a student’s mastery over complex educational domains. In this paper, we discuss how using physical robots as platforms for artificially intelligent tutors enables an expanded space of possible educational interactions. We also describe a work-in-progress to (1) extend previous work in personalized user models for robotic tutoring and (2) further explore the differences between interaction with physical robots and onscreen agents. Specifically, we are examining how embedding an tutoring interaction inside a story, game, or activity with an agent may differentially affect learning gains and engagement in interactions with physical robots and screen-based agents.

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.

As astronauts move deeper into space they must also become more autonomous from mission control on Earth. As a result, astronauts must take on additional responsibilities for jobs typically performed by flight controllers today, and crew workload and training requirements are expected to increase. Robotic automation has potential to reduce crew workload and training needs. Additionally robots with some level of autonomy can reduce human risk by per-forming hazardous tasks that crew would otherwise have to perform. We are working with NASA to investigate new concepts of operation for astronauts interacting with autonomous robots in space, including remote supervision of a planetary robot by an astronaut orbiting the planet and remote understanding of robotic activities without eyes-on monitoring. We also are developing techniques for computing and analyzing agent performance for the roles and responsibilities needed for these ConOps, and have developed software to compute these performance measures for humans and robots in-line during mission operations. We describe results of using this software to monitor rover performance during multiple NASA robotic field tests and analog mission simulations.

In this work we present a conceptual design to automatically evaluate a subject's performance for a homebased stroke rehabilitation system. We propose to model a reaching task as a trajectory in the state space of hand part features and then use reward learning to automatically generate new ratings for subjects to track performance over time. Neuromuscular rehabilitation of the upper-extremity after a stroke requires dedicated hours of arm and hand exercises with a therapist. Often to improve the flexibility of the hands, therapists ask patients to manipulate differently shaped objects and move them around. We are working towards developing a home-based neuromuscular rehabilitation system Figure 1: An ideal home based therapy system using a single by doing away with markers and using 2D computer vision camera where a subject can do upper-extremity exercises by instead. As a first step, we are able to identify different grasp manipulating objects in the home on a simple kitchen table.

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.

This paper introduces the Affordance Template framework used to supervise task behaviors on the NASA-JSC Valkyrie robot at the 2013 DARPA Robotics Challenge (DRC) Trials. This framework provides graphical interfaces to human supervisors that are adjustable based on the run-time environmental context (e.g., size, location, and shape of objects that the robot must interact with, etc.). Additional improvements, described below, inject degrees of autonomy into instantiations of affordance templates at run-time in order to enable efficient human supervision of the robot for accomplishing tasks.