A method is introduced to extract and transfer knowledge between a source and a target task in continuous domains and for direct policy search algorithms. The principle is (1) to use a direct policy search on the source task, (2) extract knowledge from the learning traces and (3) transfer this knowledge with a reward shaping approach. The knowledge extraction process consists in analyzing the learning traces, i.e. the behaviors explored while learning on the source task, to identify the behavioral features specific to successful solutions. Each behavioral feature is then attributed a value corresponding to the average reward obtained by the individuals exhibiting it. These values are used to shape rewards while learning on a target task. The approach is tested on a simulated ball collecting task in a continuous arena. The behavior of an individual is analyzed with the help of the generated knowledge bases.

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.

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.

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.

In this paper, we briefly address a research regarding how to objectively evaluate machine-based object similarity measures by human-based estimation. Based on a novel approach for similarity measure of 3-D objects we create a ground truth of 3-D objects and their similarities estimated by humans. The automatic similarity results achieved are evaluated against this ground truth in terms of precision and recall in an object retrieval scenario. To further illustrate the reciprocity properties between machine and human perception, we compare the similarities achieved by both on testing data and show how it can be used to address other problems and formulations.

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 problem of transferring skills to hyper-redundant system requires the design of new motion primitive representations that can cope with multiple sources of noise and redundancy, and that can dynamically handle perturbations in the environment. One way is to take inspiration from invertebrate systems in nature to seek for new versatile representations of motion/behavior primitives for continuum robots. In particular, the incredibly varied skills achieved by the octopus can guide us toward the design of such robust encoding scheme. This abstract presents our ongoing work that aims at combining statistical machine learning, dynamical systems and stochastic optimization to study the problem of transferring skills to a flexible surgical robot (STIFF-FLOP) composed of 2 modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion.

In this summary report I give an account of research conducted over the last two years, showing the suitability and the advantages of applying computational analogy-engines in the analysis and design of analogy-based methods and tools in teaching and education. This overview constitutes the conclusion of the first phase of a multi-stage effort trying to introduce computational models of analogy also to education and the learning sciences, thus opening up these fields to computational tools and methods not only on an instrumental level, but also in analytical, conceptual, and design-oriented studies. I locate the "analogy-engines in the classroom" research program within the bigger schemes of studying human creativity and computational creativity, provide an introduction to the theoretical underpinnings of the endeavor, and revisit three worked out case studies serving as proofs of the feasibility of the overall approach.

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.

For people constrained to picture based communication, the expression of interest in a question answering (QA) or information retrieval (IR)scenario is highly limited. Traditionally, alternative and augmentative communication (AAC) methods (such as gestures and communication boards) are utilised. But only few systems allow users to produce whole utterances or sentences that consist of multiple words; work to generate them automatically is a promising direction in the big data context.In this paper, we provide a dedicated access method for the open-domain QA and IR context. We propose a method for the user to search for additional symbols to be added to the communication board in real-time while using access to big data sources and context based filtering when the desired symbol is missing. The user can select a symbol that is associated with the desired concept and the system searches for images on the Internet - here, in Wikipedia - with the purpose of retrieving an appropriate symbol or picture. Querying for candidates is performed by estimating semantic relatedness between text fragments using explicit semantic analysis (ESA).