Human-as-a-Robot Performance in Augmented Reality Teleultrasound
Bibliographic record
Abstract
Purpose: In “human teleoperation” [1], augmented reality (AR) and haptics are used to tightly couple an expert leader to a human follower. To determine the feasibility of human teleoperation, we quantify the abil- ity of humans to track a position and/or force trajectory via AR cues. The human response time, precision, frequency response, and step response were characterized, and several rendering methods were compared. Methods: Volunteers (n=11) performed a series of tasks as the fol- lower in our human teleoperation system. The tasks involved tracking pre-recorded series of motions and forces, each time with a different rendering method. The order of tasks and rendering methods was ran- domized to avoid learning effects and bias. The volunteers then performed a series of frequency response tests and filled out a questionnaire. Results: Rendering the full ultrasound probe as a position target with an error bar displaying force led to the best position and force tracking. Following force and pose simultaneously was more difficult but did not lead to significant performance degradation versus following one at a time. On average, subjects tracked positions, orientations, and forces with rms tracking errors of 6.2 ± 1.9 mm, 5.9 ± 1.9˚, 1.0 ± 0.3 N, steady-state errors of 2.8 ± 2.1 mm, 0.26 ± 0.2 N, and lags of 345.5 ± 87.6 ms respectively. Performance decreased with input frequency, until the person could no longer follow, depending on the input amplitude. Conclusion: This paper characterizes human tracking ability in aug- mented reality human teleoperation, which shows the system’s feasi- bility and good performance, and is important for designing future human computer interfaces using augmented and virtual reality.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".