Reinforcement Learning-Based Redirection Controller for Efficient Redirected Walking in Virtual Maze Environment

Abstract

Redirected walking (RDW) is a locomotion technique used in virtual reality (VR) that enables users to explore large virtual environments in a limited physical space. Existing RDW techniques mainly work on the obstacle-free physical spaces larger than a square of four-meter sides. To improve usability, RDW techniques that work on comparatively smaller physical spaces with obstacles need to be developed. In RDW, users are restricted to the physical space by redirection techniques (RETs) that control the view of the head-mounted display. Reinforcement learning, a branch of machine learning techniques, is advantageous in designing efficient redirection controllers compared to manual design. In this paper, we propose a reinforcement learning-based redirection controller (RLRC) that aims to realize an efficient RDW in small physical spaces. The controller is trained using the simulator and is expected to select an appropriate redirection technique from the current state and route information of the virtual environment. We evaluate the RLRC with simulator and user tests in a virtual maze in several physical spaces, including a square physical space of four-meter sides with an obstacle, and a square physical space of two-meter sides. The simulator test shows that the proposed RLRC can reduce the number of undesirable redirection techniques performed compared with existing methods. The proposed RLRC is found to be effective in the square physical space of two-meter sides in the user test.