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 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 Multi-Agent Reinforcement Resetter (MARR), which resets the user taking into account both physical obstacles and multi-user movement to minimize the number of resets. MARR is trained using multi-agent reinforcement learning to determine the optimal reset direction in different environments. This approach allows MARR 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 MARR to other reset technologies through simulation tests and user studies, and found that MARR outperformed the existing methods. MARR improved performance by learning the optimal reset direction for each subtle technique used in training. MARR has the potential to be applied to new subtle techniques proposed in the future. Overall, our study confirmed that MARR is an effective reset technique in multi-user environments.

In this paper, we present the Selective Redirection Controller (SRC), which selects the optimal redirection controller based on the physical and virtual environment in Redirected Walking (RDW). The primary advantage of SRC over existing controllers is its dynamic switching among four different redirection controllers (S2C, TAPF, ARC, and SRL) based on the user's environment, as opposed to using a single fixed controller throughout the experience. By switching between redirection controllers based on the context around the user, SRC aims to optimize the advantages of each redirection strategy. The SRC model is trained using reinforcement learning to dynamically and instantaneously switch redirection controllers based on the user's environment. We evaluated the performance of SRC against traditional redirection controllers through simulations and user studies conducted in various physical and virtual environments. The findings indicate that SRC reduces the number of resets significantly compared to traditional redirection controllers. Heat map visualization was utilized during the development process to analyze which redirection controller SRC chooses based on the different environments around the user. SRC alternates between redirection techniques based on the user's environment, maximizing the advantages of each strategy for a superior RDW experience.

A simultaneous walking experience in virtual and real spaces can provide a high sense of presence. However, users may face challenges when walking within a large virtual space while walking in a small and complex real space. Several methods such as Redirected Walking (RDW) and Substitutional Reality (SR) have been proposed as different approaches to this problem. However, the users must “reset” their movement direction at unpredictable moments to avoid collision in a small and complex real space when using subtle RDW that does not maintain the correspondence between virtual and real space. Contrarily, exploration through the SR has a limitation in that the VR scene is restricted to a controlled area. In this paper, we propose Reset at Fixed Positions (RFP), a method that combines RDW with the advantage of the SR and matches walkable real space with walkable virtual space. To utilize RFP, we defined Guaranteed Space Block (GSB), a unit space that constitutes a walkable virtual space. This space is obtained through the point reflection of the GSB utilizing the reset position within the GSB. RFPs can be implemented by two methods: Generating Virtual Space Using RFP (G-RFP) and Implementing Given Virtual Space Using RFP (I-RFP). G-RFP can create an infinitely large virtual space for exploration. On the other hand, I-RFP can conFigure a given virtual environment to make users walk. We observed that G-RFP provides higher presence, immersion and a higher mean distance traveled between resets compared to the existing RDW method in a complex real space through a user study. In addition, exploration through I-RFP provided a higher immersion, a comparable presence, and a similar number of resets.

In multi-user Redirected Walking (RDW), the space subdivision method divides a shared physical space into sub-spaces and allocates a sub-space to each user. While this approach has the advantage of precluding any collisions between users, the conventional space subdivision method suffers from frequent boundary resets due to the reduction of available space per user. To address this challenge, in this study, we propose a space subdivision method called Optimal Space Partitioning (OSP) that dynamically divides the shared physical space in real-time. By exploiting spatial information of the physical and virtual environment, OSP predicts the movement of users and divides the shared physical space into optimal sub-spaces separated with shutters. Our OSP framework is trained using deep reinforcement learning to allocate optimal sub-space to each user and provide optimal steering. Our experiments demonstrate that OSP provides higher sense of immersion to users by minimizing the total number of reset counts, while preserving the advantage of the existing space subdivision strategy: ensuring better safety to users by completely eliminating the possibility of any collisions between users beforehand. Our project is available at https://github.com/AppleParfait/OSP-Archive.

Most virtual reality (VR) experiences are held in limited physical space; therefore, increasing the physical space's spatial efficiency is an essential task for the VR industry. Redirected walking maps a virtual path and a real path with unnoticeable distortion, enabling users to walk through a much bigger virtual space than physical space. To hide the distortion from the user, detection thresholds have been measured, entirely focusing on forward steps. However, it is not uncommon for the user to walk non-forward, that is, sideward and backward in VR. In addition to a forward step, adding options for a non-forward step can expand the VR locomotion in any direction. In this work, we measure the translation and curvature detection thresholds for non-forward steps. The results show similar translation detection thresholds with forward-step and wider detection thresholds for the curvature gain in both backward and sideward step experiments. Having sideward and backward steps in the redirected walking arsenal can add freedom to virtual world design and lead to efficient space usage.

Redirected Walking (RDW) enables users to walk in both virtual and physical tracking spaces simultaneously, which is an effective method to increase presence in Virtual Reality (VR). Recently, RDW technologies have been developed in a multi-user environment where multiple users share the same physical tracking space and simultaneously explore the same virtual space. Meanwhile, in the Steer-To-Optimal-Target (S2OT) method, user actions are planned in RDW by introducing machine learning models such as reinforcement learning. In this paper, we propose a new predictive RDW algorithm "Multiuser-Steer-to-Optimal-Target (MS2OT)" that extends the S2OT method into an environment with multiple users and various types of tracking space. In addition to the steering actions used in S2OT, MS2OT considers pre-reset actions and uses more steering targets and an improved reward function. The locations of multiple users and tracking space information are treated as visual information to be the state of the reinforcement learning model in MS2OT. Hence, the artificial neural network of a multilayer three-dimensional convolutional neural network with a dueling double deep network architecture is learned through Q-Learning. MS2OT significantly reduces the total number of resets compared to the conventional RDW algorithms such as S2C and APF-RDW in a multi-user environment and improves the total distance and average distance between resets during the same period. Experimental results show that MS2OT can process up to 32 users in real-time.

Various studies have been conducted to realize realistic direct interaction in the virtual environment. In this study, we focus on a situation wherein two users using the same physical space explore the same virtual environment using redirected walking (RDW) technology. For two users to meet each other in a virtual environment to realize realistic direct interaction, they must simultaneously meet each other in physical space. However, if the RDW algorithm is applied to each user independently, the relative positions and orientations of the two users can be significantly different in the virtual and physical spaces. We present a recovery algorithm that adjusts the relative position and orientation such that they become the same in the two spaces. Our recovery algorithm uses either modified subtle RDW techniques or overt recovery techniques in three cases depending on the relative position and orientation of the two users. Once the recovered state is reached, the two users can go forward to meet each other and directly interact in the virtual and physical spaces simultaneously. Based on the experiment results, we can confirm that the application of our recovery technology to the system increases the user’s satisfaction in usability and the presence of coexistence in the virtual environment with other users.

In this paper, we propose a new visual motion delay effect to enhance the presence of a user in a virtual underwater experience. To do this, we simulate the resistance in the underwater environment by delaying the hand and head movements of the user's avatar. The motion delay effect is implemented using two components: a drag force and a recovery force. The experimental results show that the combination of a drag force and a recovery force creates a realistic illusion of an underwater experience and enhances the user's presence, satisfaction, and immersion in the virtual underwater environment.

This work examines the differences of the effects between using humanoid and non-humanoid avatars on the user's Illusion of Virtual Body Ownership (IVBO) and experience. We used three kinds of avatars: bipedalism group (human), quadrupedalism group (wolf), and serpentine motion group (snake). The result shows that using non-humanoid avatars feel more sense of change of their body. Users feel more proficient when using the humanoid avatar, but are more pleased with the non-humanoid avatars.

The chord progression is a fundamental building block in music which sketches the overall mood of a song. Many composers compose music by first deciding chord progressions as a structure and then adding melody and details. Despite its importance, it is rarely used as an emotional feature in music emotion recognition. Few previous works considered chords or intervals as features but the progression or transition of chords were ignored. In this work, we explore the effect of chord progressions in music emotion recognition. We collected music database and extracted features to form an emotion recognition model. The chord progression is then detected from each song, and its effectiveness is showed using cross-validation. The results show that chord progressions have influence in music emotion, especially valence.