This year, the Robotics Exhibition included two such robotics challenge problems: manipulation and learning by demonstration. In the Small-Scale Manipulation Challenge four teams demonstrated systems playing robotic chess. This exhibit was organized by David Touretzky and Monica D. Anderson. In the Learning by Demonstration Challenge, three teams demonstrated systems learning a block-sorting task. This exhibit was organized by Sonia Chernova. Additionally, this year marked another successful turnout for the Robotics Education Track, organized by Zachary Dodds, which highlights student-and educator-led robotics projects. In this article we give a summary of these three components of the exhibition.

Current behavior authoring tools force game designers to split their attention between the game context and the tool context. We have addressed this problem by developing a behavior authoring tool that merges these two contexts. This paper outlines the design and implementation of a gameindependent behavior tree authoring tool, called AIPaint, that allows a designer to create and edit behavior trees via a natural sketching interface overlaid on the game world. We demonstrate the use of AIPaint to author computercontrolled characters in two simple games and report on an observational evaluation.

An important aspect of Intelligent Tutoring Systems is providing assistance to students as well as assessing them. The standard state-of-the-art algorithms (Knowledge Tracing and Performance Factor Analysis) for tracking student knowledge, however, only look at the correctness of student first response and ignore the amount of assistance students needed to eventually answer the question correctly. In this paper, we propose the Assistance Model (AM) for predicting student performance using information about the number of hints and attempts a student needed to answer the previous question. We built ensemble models that combine the state-of-the-art algorithms and the Assistance Model together to see if the Assistance Model brings improvements. We used an ASSISTments dataset of 200 students answering a total of 4,142 questions generated from 207 question templates. Our results showed that the Assistance Model did in fact reliably increase predictive accuracy when combined with the state-of-the-art algorithms.

We have developed an automated counseling system in which clients interact with an embodied conversational People who could benefit from a positive change in health agent (Cassell 2000) that acts as a virtual counselor. To behavior form a large and variable population, with assist precontemplations and contemplators, we differences in individual characteristics and circumstances.

This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration.

We summarize our past, present and future research related to human-robot dialogue, starting with its foundations in collaborative discourse theory, continuing to our current research on recognizing and generating engagement, and concluding with an outline of new work we are beginning on the modeling of long-term relationships between humans and robots.

We have developed and demonstrated an experimental authoring and run-time tool, called Disco for Games, that supports the creation of games in which dialogue and action are integrated without the need for changing modes. This tool is based on collaborative discourse theory and hierarchical task networks, in which utterances are treated as actions, and has a number of additional benefits including better modeling of interruptions, automatic dialogue generation, plan recognition and automatic failure retry.

This paper discusses the development of a sequence of undergraduate courses forming the core curriculum in the Robotics Engineering (RBE) B.S. program at Worcester Polytechnic Institute (WPI). The laboratory robotics kit developed for the junior-level courses is presented in detail. The platform is designed to be modular and cost-effective and it is suitable for laboratory based robotics education. The system is ideal not only for undergraduate coursework but also may be adapted for graduate and undergraduate research as well as for exposing K-12 students to STEM.

Demonstration materials can take the research and industry presentations, project demonstrations, (Path planning, animation / camera control, tactical / strategic form of a recorded demonstration session, an executable version interactive poster sessions, and product exhibits.