PAWS: Personalized Arm and Wrist Movements With Sensitivity Mappings for Controller-Free Locomotion in Virtual Reality
Why this work is in the frame
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Bibliographic record
Abstract
Virtual Reality (VR) headsets equipped with multiple cameras enable hands-only teleportation techniques without requiring any physical controller. Hands-only teleportation is an effective alternative to controllers for navigation tasks in virtual reality - allowing users to move from one point to another instantaneously. However, the current implementation of hands-only techniques does not consider users' physical attributes (e.g., arm's reach). Thus, a hands-only teleportation technique can lead to different user experiences based on physical attributes. We propose PAWS, a personalized arm and wrist-based teleportation technique that incorporates users' physical attributes for improved teleportation experiences. We first evaluate different degrees of teleportation personalization with no-, partial, and full personalization. We find that full personalization offers faster locomotion - but at the cost of degraded performances with distant targets due to increased sensitivity. We hence further explore different combinations of mapping functions (e.g., sigmoid, quadratic) to personalize motor movements and find that asymmetric functions result in improved performance. Overall, our results show that PAWS helps users to navigate quickly in virtual environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it