From “skype on wheels” to embodied telepresence: a holistic approach to improving the user experience of telepresence robots
Bibliographic record
Abstract
Telepresence robots offer the promise of remote presence, but user experience, usability, and performance challenges hinder widespread adoption. This study introduces a novel and low-cost user interface for telepresence robots that integrates insights from virtual reality (VR) and robotics to address these limitations. The novel setup was designed holistically, considering several different factors: an inclined rotating chair for embodied rotation, a joystick for precise translation, dual displays for enhanced spatial awareness, and an immersive setup with controlled lighting and audio. A user study (N = 42) with a simulated robot in a virtual environment compared this novel setup with a standard setup, that mimicked the typical user interface of commercial telepresence robots. Results showed that this novel setup significantly improved the user experience, particularly increasing presence, enjoyment, and engagement. This novel setup also improved task performance over time, reducing obstacle collisions and distance traveled. These findings highlight the potential for combining and incorporating insights from VR and robotics to design more effective and user-friendly interfaces for telepresence robots, paving the way for increased adoption. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-025-01222-0.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".