F-RDW: Redirected Walking With Forecasting Future Position.

In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applications. To tackle this challenge, we propose a mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions by using the conventional method, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space, and the following step feeds those predicted values into existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting their internal mechanism in applicable ways. The results of our simulation test and user study demonstrate the significance of future information when using RDW in small physical spaces or complex environments. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets, hence augmenting the redirection performance of all RDW methods explored in this work. Our project and dataset are available at https://github.com/YonseiCGnA-VR/F-RDW.

Redirection Strategy Switching: Selective Redirection Controller for Dynamic Environment Adaptation.

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.

Locomotion Techniques for Dynamic Environments: Effects on Spatial Knowledge and User Experiences.

Various locomotion techniques are used to navigate and find way through space in virtual environments (VE), and each technique provides different experiences and performances to users. Previous studies have primarily focused on static environments, whereas there is a need for research from a different perspective of dynamic environments because there are many moving objects in VE, such as other users. In this study, we compare the effects of different locomotion techniques on the user's spatial knowledge and experience, depending on whether the virtual objects are moving or not. The investigated locomotion techniques include joystick, teleportation, and redirected walking (RDW), all commonly used for VR navigation. The results showed that the differences in spatial knowledge and user experience provided by different locomotion techniques can vary depending on whether the environment is static or dynamic. Our results also showed that for a given VE, there are different locomotion techniques that induce fewer collisions between the user and other objects, or reduce the time it takes the user to perform a given task. This study suggests that when designing a locomotion interface for a specific VR application, it is possible to improve the user's spatial knowledge and experience by recommending different locomotion techniques depending on the degree of environment dynamism and and type of task.

MARR : A Multi-Agent Reinforcement Resetter for Redirected Walking.

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.

Persistence of metric biases in body representation during the body ownership illusion.

Our perception of the body's metric is influenced by bias according to the axis, called the systematic metric bias in body representation. Systematic metric bias was first reported as Weber's illusion and observed in several parts of the body in various patterns. However, the systematic metric bias was not observed with a fake hand under the influence of the body ownership illusion during the line length judgment task. The lack of metric bias observed during the line length judgment task with a fake hand implies that the tactile modality occupies a relatively less dominant position than perception occurring through the real body. The change in weight between visual and tactile modalities during the body ownership illusion has not been adequately investigated yet, despite being a factor that influences the perception through body ownership illusion. Therefore, this study aimed to investigate whether the dominance of vision over tactile modality is prominent, regardless of the task type. To investigate whether visual dominance persists during the process of inducing body ownership illusion regardless of task type, we introduced spatial visuotactile incongruence (2 cm, 3 cm) in the longitudinal and transverse axes during the visuotactile localization tasks and measured the intensity of the body ownership illusion using a questionnaire. The results indicated that participants perceived smaller visuotactile incongruence when the discrepancy occurred in the transverse axis rather than in the longitudinal axis. The anisotropy in the tolerance of visuotactile incongruence implies the persistence of metric biases in body representation. The results suggest the need for further research regarding the factors influencing the weight of visual and tactile modalities.