Multi-user Reset Controller for Redirected Walking Using Reinforcement Learning

TL;DR

This paper introduces a multi-user reset controller for redirected walking that uses reinforcement learning to minimize resets by considering environmental obstacles and user movements, improving immersion.

Contribution

It proposes a novel multi-agent reinforcement learning-based reset controller specifically designed for multi-user environments in redirected walking.

Findings

MRC reduces the mean number of resets by up to 55%.

MRC effectively accounts for environmental obstacles and user movements.

Simulation and user studies confirm MRC's effectiveness in multi-user settings.

Abstract

The reset technique of Redirected Walking (RDW) forcibly reorients the user's direction overtly to avoid collisions with boundaries, obstacles, or other users in the physical space. However, excessive resetting can decrease the user's sense of immersion and presence. Several RDW studies have been conducted to address this issue. Among them, much research has been done on reset techniques that reduce the number of resets by devising reset direction rules (e.g.,~ 2:1-turn, reset-to-center) or optimizing them for a given environment. However, existing optimization studies on reset techniques have mainly focused on a single-user environment. In a multi-user environment, the dynamic movement of other users and static obstacles in the physical space increase the possibility of resetting. In this study, we propose a multi-user reset controller (MRC) that resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MRC is trained using multi-agent reinforcement learning to determine the optimal reset direction in different environments. This approach enables MRC to effectively account for different environmental contexts, including arbitrary physical obstacles and the dynamic movements of other users in the same physical space. We compared MRC with other reset techniques through simulation tests and user studies, and our results show that MRC reduces the mean number of resets by up to 55\%. Overall, our study confirmed that MRC is an effective reset technique in multi-user environments. Supplemental materials are available at an anonymous link: (https://osf.io/rpftu/?view_only=8230f344502f4013af2a5229db5e21c3).