Improving Spatial Perception in Telepresence and Teleaction Systems by Displaying Distance Information through Visual and Vibrotactile Feedback
Bibliographic record
Abstract
Telepresence and teleaction (TPTA) systems enable humans to operate in a remote, hostile, or inaccessible environment. The performance of these systems strongly depends on the deployed sensors and actuators and the quality of the feedback to the user. Spatial perception plays an especially important role when handling dangerous and fragile objects. Stereoscopic cameras and displays can be deployed to improve spatial perception. However, in networked TPTA scenarios with limited transmission capacity on the communication link, the additional bandwidth required for sending two separate video streams is often infeasible. Furthermore, stereoscopic displays are known to have limitations in quality that affect spatial orientation when navigating within the remote environment. In this work, we present methods for displaying remotely measured distance between a teleoperator and a target object through visual and vibrotactile displays in order to improve spatial perception in TPTA systems. Furthermore, we propose to exploit human sensory illusions of the vibrotactile sense to overcome limitations of vibrotactile displays. Psychophysical experiments are conducted to investigate the performance of our proposed display methods. Our experiments show that our proposed vibrotactile feedback methods can compete with visual distance displays.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".