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.

Body-centric locomotion allows users to control both movement speed and direction with body parts (e.g., head tilt, arm swing or torso lean) to navigate in virtual reality (VR). However, there is little research to systematically investigate the effects of body parts for speed and direction control on virtual locomotion by taking in account different transfer functions(L: linear function, P: power function, and CL: piecewise function with constant and linear function). Therefore, we conducted an experiment to evaluate the combinational effects of the three factors (body parts for direction control, body parts for speed control, and transfer functions) on virtual locomotion. Results showed that (1) the head outperformed the torso for movement direction control in task completion time and environmental collisions; (2) Arm-based speed control led to shorter traveled distances than both head and knee. Head-based speed control had fewer environmental collisions than knee; (3) Body-centric locomotion with CL function was faster but less accurate than both L and P functions. Task time significantly decreased from P, L to CL functions, while traveled distance and overshoot significantly increased from P, L to CL functions. L function was rated with the highest score of USE-S, -pragmatic and -hedonic; (4) Transfer function had a significant main effect on motion sickness: the participants felt more headache and nausea when performing locomotion with CL function. Our results provide implications for body-centric locomotion design in VR applications.