Adaptive Redirection: A Context-Aware Redirected Walking Meta-Strategy

Previous research has established redirected walking as a potential answer to exploring large virtual environments via natural locomotion within a limited physical space. However, much of the previous work has either focused on investigating human perception of redirected walking illusions or developing novel redirection techniques. In this paper, we take a broader look at the problem and formalize the concept of a complete redirected walking system. This work establishes the theoretical foundations for combining multiple redirection strategies into a unified framework known as adaptive redirection. This meta-strategy adapts based on the context, switching between a suite of strategies with a priori knowledge of their performance under the various circumstances. This paper also introduces a novel static planning strategy that optimizes gain parameters for a predetermined virtual path, known as the Combinatorially Optimized Pre-Planned Exploration Redirector (COPPER). We conducted a simulation-based experiment that demonstrates how adaptation rules can be determined empirically using machine learning, which involves partitioning the spectrum of contexts into regions according to the redirection strategy that performs best. Adaptive redirection provides a foundation for making redirected walking work in practice and can be extended to improve performance in the future as new techniques are integrated into the framework.

Automated summary

This research aims to combine multiple redirection strategies into an adaptive framework for natural locomotion in large virtual environments. The authors determine adaptation rules through machine learning and simulation-based experiments. They also introduce a static planning strategy to optimize redirection effects.