Andrea Lockerd

Cheese: tracking mouse movement activity on websites, a tool for user modeling

Conventional web interfaces respond to and consider only mouse clicks when defining a user model. We have extended this and take into account all mouse movements on a page as an additional layer of information for inferring user interest. We have developed a straightforward way to record all mouse movements on a page, and conducted a user study to analyze and investigate mouse behavior trends. We found certain mouse behaviors, common across many users, which are useful for content providers in increasing the effectiveness of their interface design.

TUTELAGE AND COLLABORATION FOR HUMANOID ROBOTS

This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robots ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in peoples daily lives.

Tutelage and socially guided robot learning

We view the problem of machine learning as a collaboration between the human and the machine. Inspired by human-style tutelage, we situate the learning problem within a dialog in which social interaction structures the learning experience, providing instruction, directing attention, and controlling the complexity of the task. We present a learning mechanism, implemented on a humanoid robot, to demonstrate that a collaborative dialog framework allows a robot to efficiently learn a task from a human, generalize this ability to a new task configuration, and show commitment to the overall goal of the learned task. We also compare this approach to traditional machine learning approaches.

Teaching and Working with Robots as a Collaboration

New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as capable members of human-robot teams for security, military, and space efforts. These applications require robots to be able to quickly learn how to perform new tasks from natural human instruction, and to perform tasks collaboratively with human teammates. Using joint intention theory as our theoretical framework, our approach integrates learning and collaboration through a goal based task structure. Specifically, we use collaborative discourse with accompanying gestures and social cues to teach a humanoid robot a structurally complex task. Having learned the representation for the task, the robot then performs it shoulder-to-shoulder with a human partner, using social communication acts to dynamically mesh its plans with those of its partner, according to the relative capabilities of the human and the robot.

Eye-R, a glasses-mounted eye motion detection interface

Eye-R is a system designed to detect and communicates the intentional information conveyed in eye movement. This glasses-mounted, wireless device stores and transfers information based on user eye motion and external IR devices thus promoting an enriched experience with their environment. This paper describes how the system measures eye motion and utilizes this as an implicit input channel to a sensor system and computer. In the primary scenario, eye motion detection is used to recognize a users gaze. When the persons eyes are fixated the system infers that they are paying attention to something in their environment and then tries to facilitate an exchange of information in either direction on the users behalf.

LAFCam: Leveraging affective feedback camcorder

If a video camera recognizes and records affective data from the camera operator, this data can help determine which sequences will be interesting to the camera operator at a later time. In the case of home videos, the camera operator is likely to also be the editor and narrator of the final video. LAFCam is a system for recording and editing home video. We facilitate the process of browsing and provide automatic editing features by indexing where the camera operator laughed and visualizing the skin conductivity and facial expressions in the editing session.

Working collaboratively with humanoid robots

This paper presents an overview of our work towards building humanoid robots that can work alongside people as cooperative teammates. We present our theoretical framework based on a novel combination of joint intention theory and collaborative discourse theory, and demonstrate how it can be applied to allow a human to work cooperatively with a humanoid robot on a joint task using speech, gesture, and expressive cues. Such issues must be addressed to enable many new and exciting applications for humanoid robots that require them to assist ordinary people in daily activities or to work as capable members of human-robot teams.

Building an autonomous humanoid tool user

To make the transition from a technological curiosity to productive tools, humanoid robots will require key advances in many areas, including, mechanical design, sensing, embedded avionics, power, and navigation. Using the NASA Johnson Space Centers Robonaut as a testbed, the DARPA mobile autonomous robot software (MARS) humanoids team is investigating technologies that will enable humanoid robots to work effectively with humans and autonomously work with tools. A novel learning approach is being applied that enables the robot to learn both from a remote human teleoperating the robot and an adjacent human giving instruction. When the remote human performs tasks teleoperatively, the robot learns the salient sensory-motor features to executing the task. Once learned, the task may be carried out fusing the skills required to perform the task, guided by on-board sensing. The adjacent human takes advantage of previously learned skills to sequence the execution of these skills. Preliminary results from initial experiments using a drill to tighten lug nuts on a wheel are discussed.

Mr.Web: an automated interactive webmaster

This paper describes a system, Mr.Web, designed to interact with users over email to create and update Web pages. Our goal is that users interact with Mr.Web as if it were a human Webmaster. We collected 325 examples of people writing email requests to a Webmaster, and used this to generate the semantics of Mr.Webs email parser. The results of the survey indicate that the limited context of a Webmaster gives us a reasonable subset of the natural language processing (NLP) problem. This paper explains the system design, user study results, and plans for future work.