Fast Synthetic Vision, Memory, and Learning Models
for Virtual Humans
- James Kuffner, Jr.
- Jean-Claude Latombe
- Robotics Laboratory
- Department of Computer Science
- Stanford University
- Stanford, CA 94305, USA
Abstract
This paper presents a simple and efficient method of modeling
synthetic vision, memory, and learning for autonomous animated
characters in real-time virtual environments. The model is efficient
in terms of both storage requirements and update times, and can be
flexibly combined with a variety of higher-level reasoning modules or
complex memory rules. The design is inspired by research in motion
planning, control, and sensing for autonomous mobile robots. We apply
this framework to the problem of quickly synthesizing from navigation
goals the collision-free motions for animated human figures in
changing virtual environments. We combine a low-level path planner, a
path-following controller, and cyclic motion capture data to generate
the underlying animation. Graphics rendering hardware is used to
simulate the visual perception of a character, providing a feedback
loop to the overall navigation strategy. The synthetic vision and
memory update rules can handle dynamic environments where objects
appear, disappear, or move around unpredictably. The resulting model
is suitable for a variety of real-time applications involving
autonomous animated characters.
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