Usability evaluation of emerging virtual reality technologies in telerobotics
Bibliographic record
Abstract
Recent technological advancements in robotic systems have led to increasing exposure of humans to these systems. Therefore, the quality of Human-Robot Interactions (HRI), which effect adoption of robots by human operators, is increasingly important [1]. This is particularly true in telerobotic applications, where users can only operate a robot remotely and a problematic interface can have major implications. Therefore, the objective of this study is to investigate whether using a head-mounted display and a hand-tracking device in the development of HRI systems can result in better interactions. Hence, usability testing was performed via user studies considering three factors: effectiveness, efficiency, and user satisfaction. Three different HRI modes were developed: manual, Virtual Reality (VR), and Mixed Reality (MR). The manual mode involved manual control of a robotic arm using a keyboard with visual feedback on a monitor. The VR mode entailed controlling the robotic arm by hand gestures using Leap Motion and visual feedback from a VR environment provided through Oculus Rift. The MR mode was similar to the VR, but the visual feedback was augmented with views from two cameras provided to the user through Oculus Rift. These modes were used to perform a pick-and-place task. Effectiveness was evaluated by measuring the success rates of each mode and performing an error analysis; efficiency was examined by considering the time-of-completion; and user satisfaction was subjectively measured using a Likert-type questionnaire. Statistical analysis revealed that the VR mode was the most efficient and effective and also resulted in fewer human and system errors. Users' gaming experience was also considered as a factor, but had no effect on the effectiveness or efficiency of any mode. Therefore, it is concluded that users require no prior experience, and consequently training, to achieve effective and efficient HRI using this mode. Moreover, the questionnaire revealed that users were highly satisfied with both VR and MR modes, with no clear preference. To conclude, the application of Oculus Rift and Leap Motion results in more effective and efficient HRI, and is recommended for use in real-world applications similar to pick-and place tasks, such as in the manufacturing industry.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".