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Record W4302305922 · doi:10.1145/3526114.3561350

UltraBots: Large-Area Mid-Air Haptics for VR with Robotically Actuated Ultrasound Transducers

2022· preprint· en· W4302305922 on OpenAlexaff
Mehrad Faridan, Marcus Friedel, Ryo Suzuki

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHaptic technologyComputer scienceWorkspaceRobotScalabilityVirtual realityRendering (computer graphics)SimulationHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.055
GPT teacher head0.301
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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