We demonstrate a system that combines steerable projection and computer vision technologies to create "touch-screen" style interactive displays on any flat surface in a space. A high-end version of the system -- the Everywhere Display (ED) -- combines an LCD projector with motorized focus and zoom, a computer controlled pan-tilt mirror, and a pan-tilt zoom camera to enable steering of interactive projections around space. A low-end version (ED-lite) enables creation of interactive displays using a portable projector and camera attached to a laptop computer. Unlike traditional augmented reality systems, the ED systems enable delivery of interactive multimedia content on ordinary objects without requiring users to wear head mounted displays or carry special input devices.

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

Traditionally, a user has positioned himself/herself to be in front of a display in order to access information from it. In this information age, life at work and even at home is often confined to be in front of a display device that is the source of information or entertainment. The paper introduces another paradigm where the display follows the user rather than the user being tied to the display. We demonstrate how steerable projection and people tracking can be combined to achieve a display that automatically follows the user.

The authors provide an informal characterization of the process of workcell application design and describe the architecture and implementation of a software environment called WADE (workcell application development) intended to aid a designer of workcell applications. WADE embodies a reasonably general approach for modeling a wide range of industrial equipment, programming them in as powerful a manner as their control languages, simulating workcell applications with multiple programmable and nonprogrammable equipment, and analyzing the performance in flexible ways. Its design is object-oriented and open-ended, suitable for easy experimentation, extension, and refinement. The implementation is based on a modern language called AML/X with object-oriented programming capabilities and a powerful and production-quality solid modeling system called GDP. The authors illustrate the functionality of WADE with two example applications and conclude with a brief discussion of their experiences in designing and building WADE and some issues for consideration in future work.>

Robots are increasingly being used in situations requiring them to exhibit flexible behavior. In such situations point-to-point motion sequences are inadequate. Instead, robots will probably use planning mechanisms, coupled with sensors of various kinds, to carry out their tasks. Such mechanisms allow robots to perform in situations where object position cannot be exactly specified in advance and in which unanticipated events can occur. One alternative is to teach the robot new plans. This paper describes a robot assembly system in which the robot is taught the steps in the new plan using natural language input.

Existing techniques for programming robots are either limited or require expert programmers. This paper describes an implemented robot system, TL, which is taught using a new technique which is easy to use yet produces powerful high-level plans. In this technique the user provides an example of the plan by moving the manipulator through the desired assembly. TL observes the assembly and infers the underlying plan. This paper describes TL, presents an example of it learning to assemble a simple electric switch, and discusses future directions for development.