Interface design and usability analysis for a robotic telepresence platform
Bibliographic record
Abstract
With the rise in popularity of robot-mediated teleconference (telepresence) systems, there is an increased demand for user interfaces that simplify control of the systems' mobility. This is especially true if the display/camera is to be controlled by users while remotely collaborating with another person. In this work, we compare the efficacy of a conventional keyboard and a non-contact, gesture-based, Leap interface in controlling the display/camera of a 7-DoF (degrees of freedom) telepresence platform for remote collaboration. Twenty subjects participated in our usability study where performance, ease of use, and workload were compared between the interfaces. While Leap allowed smoother and more continuous control of the platform, our results indicate that the keyboard provided superior performance in terms of task completion time, ease of use, and workload. We discuss the implications of novel interface designs for telepresence applications.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".