Low-level Voice and Hand-Tracking Interaction Actions: Explorations with Let's Go There
Bibliographic record
Abstract
Hand-tracking allows users to engage with a virtual environment with their own hands, rather than the more traditional method of using accompanying controllers in order to operate the device they are using and interact with the virtual world. We seek to explore the range of low-level interaction actions and high-level interaction tasks and domains can be associated with the multimodal hand-tracking and voice input in VR. Thus, we created Let's Go There, which explores this joint-input method. So far, we have identified four low-level interaction actions which are exemplified by this demo: positioning oneself, positioning others, selection, and information assignment. We anticipate potential high-level interaction tasks and domains to include customer service training, social skills training, and cultural competency training (e.g. when interacting with older adults). Let's Go There, the system described in this paper, had been previously demonstrated at CUI 2020 and MobileHCI 2021. We have since updated our approach to its development to separate it into low- and high-level interactions. Thus, we believe there is value in bringing it to MobileHCI again to highlight these different types of interactions for further showcase and discussion.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".