Peter N. Prokopowicz

Gesture recognition using the Perseus architecture

Communication involves more than simply spoken information. Typical interactions use gestures to accurately and efficiently convey ideas that are more easily expressed with actions than words. A more intuitive interface with machines should involve not only speech recognition, but gesture recognition as well. One of the most frequently used and expressively powerful gestures is pointing. It is far easier and more accurate to point to an object than give a verbal description of its location. To produce a more efficient, accurate, and natural human-machine interface we use the Perseus architecture to interpret the pointing gesture. Perseus uses a variety of techniques to reliably solve this complex visual problem in non-engineered worlds. Knowledge about the task and environment is used at all stages of processing to best interpret the scene for the current situation. Once the visual operators are chosen, contextual knowledge is used to tune them for maximal performance. Redundant interpretation of the scene provides robustness to errors in interpretation. Fusion of independent types of information results in increased tolerance when assumptions about the environment fail. Windows of attention are used to improve speed and remove distractions from the scene. Furthermore, reuse is a major issue in the design of Perseus. Information about the environment and task is explicitly represented so it can easily be re-used in tasks other than pointing. A clean interface to Perseus is provided for symbolic higher level systems like the RAP reactive execution system. In this paper we describe Perseus in detail and show how it is used to locate objects pointed to by people.

Task and Environment-Sensitive Tracking,

Abstract : In a mobile robot, visual tracking, like other visual behaviors, takes place in a context that includes aspects of the task, the object being tracked, and the background. In this work, prior knowledge of those task and target characteristics that either enable or hinder different real-time image-tracking algorithms, together with run-time evaluation of the robots environment, are used to select an algorithm appropriate to the context.

Programming CHIP for the IJCAI-95 Robot Competition

The University of Chicagos robot, CHIP, is part of the Animate Agent Project, aimed at understanding the software architecture and knowledge representations needed to build a general-purpose robotic assistant. CHIPs strategy for the Office Cleanup event of the 1995 Robot Competition and Exhibition was to scan an entire area systematically and, as collectible objects were identified, pick them up and deposit them in the nearest appropriate receptacle. This article describes CHIP and its various systems and the ways in which these elements combined to produce an effective entry to the robot competition.