It's Over There: Designing an Intelligent Virtual Agent That Can Point Accurately into the Real World
Bibliographic record
Abstract
It is challenging to design an intelligent virtual agent (IVA) that can point from the virtual to the real world and have users accurately recognize where it is pointing due to differences in perceptual cues between the two spaces. We designed an IVA with factors including: a situated display, appearance, and pointing gesture strategy to establish whether it is possible to have an IVA point accurately into the real world. With a real person pointing as a baseline, we performed an empirical study using our designed IVA and demonstrated that participants perceived the IVA's pointing to a physical location with comparable accuracy to a real person baseline. Specifically, we found that when the IVA is 230 cm away from the targets on average, the IVA outperformed the real person in the vertical dimension (10.22 cm, 28.8% less error) and achieved the same level of accuracy (11.58 cm) horizontally. Our integrated design choices provide a foundation for design factors to consider when designing IVAs for pointing and pave the way for future studies and systems in providing accurate pointing perception.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".