PAWS: Personalized Arm and Wrist Movements With Sensitivity Mappings for Controller-Free Locomotion in Virtual Reality
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".