UltraBots: Large-Area Mid-Air Haptics for VR with Robotically Actuated Ultrasound Transducers
Bibliographic record
Abstract
We introduce UltraBots, a system that combines ultrasound haptic feedback and robotic actuation for large-area mid-air haptics for VR. Ultrasound haptics can provide precise mid-air haptic feedback and versatile shape rendering, but the interaction area is often limited by the small size of the ultrasound devices, restricting the possible interactions for VR. To address this problem, this paper introduces a novel approach that combines robotic actuation with ultrasound haptics. More specifically, we will attach ultrasound transducer arrays to tabletop mobile robots or robotic arms for scalable, extendable, and translatable interaction areas. We plan to use Sony Toio robots for 2D translation and/or commercially available robotic arms for 3D translation. Using robotic actuation and hand tracking measured by a VR HMD (ex: Oculus Quest), our system can keep the ultrasound transducers underneath the user’s hands to provide on-demand haptics. We demonstrate applications with workspace environments, medical training, education and entertainment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".