Exploring Plausible Preference of Body-centric Locomotion with Reinforcement Learning in Virtual Reality

Investigating users' plausible preferences for body-centric locomotion can help researchers better understand the impact of different factors on this locomotion method and optimize the locomotion configuration for providing a plausible locomotion experience. In this paper, we propose to evaluate users' plausible preferences for body-centric locomotion using a reinforcement learning method. This method can intelligently infer users' plausible preferences for different factor levels involved in the virtual locomotion by proposing possible modifications to the factor levels and asking users to accept or reject the modifications after experiencing the locomotion. We conducted a within-subject experiment to examine the impact of different factors (i.e., body parts used for virtual locomotion, the point of view, auditory feedback, the transfer function, and the coefficients of the transfer function) on users' plausible preferences for body-centric locomotion in sitting and standing postures. The results mainly indicated that (1) users preferred using arm swinging in standing posture, whereas they preferred using head tilting in sitting posture; (2) The point of view was identified as the most important factor in standing posture, while it was less important in sitting posture; (3) Participants showed consistent plausible preferences for auditory feedback, transfer function, and coefficient of the transfer function in both standing and sitting postures. Our research findings can guide the design of VR applications to enhance a plausible walking experience in different postures.