Comparative Studies on Navigation Performance using Haptic and Visual Feedback for Teleoperated Vehicles
Bibliographic record
Abstract
Teleoperating mobile robots is pivotal for tackling missions in hazardous or inaccessible environments, safeguarding human operators. Teleoperating robots in hazardous environments can benefit from haptic feedback. This study investigates the influence of haptic feedback in suboptimal visual conditions, akin to real-world scenarios where visual feedback may be compromised. Participants control a UGV using haptic devices through four different sensory feedback conditions, ranging from ideal visual and haptic feedback to degraded visual feedback with no force feedback. Contrary to expectations, the study reveals that while full visual feedback enhances navigation performance, the presence of haptic feedback often leads to worsened performance, especially in situations with impaired visual feedback. Participants tend to spend more time navigating inefficiently with haptic feedback, suggesting overcompensation due to heightened control perception. Despite these performance changes, perceived workload remains relatively unaffected. The study emphasizes the intricate interplay between visual and haptic feedback in teleoperation. It highlights the necessity for further research to comprehensively grasp the effects of these feedback modalities in real-world scenarios, where conditions are far from perfect and dynamically changing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".