A Mobile Natural Interactive Technique With Bare Hand Manipulation and Unrestricted Force Feedback for Virtual Assembly Tasks
Bibliographic record
Abstract
Most existing virtual assembly technologies cannot effectively unify the operating and feedback areas in both visualization and haptics with a large mobile workspace. Therefore, it is difficult to ensure high interactive naturalness and operational efficiency in a large assembly space. To address these problems, we propose a natural mobile interactive virtual assembly method with bare hand manipulation and unrestricted force feedback (MIVAM-BHM-UFF), covering a mobile interaction method, an unrestricted force feedback (UFF) method, and a bare hand manipulation (BHM) method. The mobile interaction method is developed to automatically track and capture the operator’s gestures in a moving way, expanding the interactive space. The UFF method is designed to provide a noncontact electromagnetic feedback force, eliminating the limitation of the traditional force feedback device and improving the interactive naturalness. A BHM method is proposed to improve interactive efficiency by unifying the operational area and feedback area. To verify the effectiveness of MIVAM-BHM-UFF, five assembly experiments were carried out. Experimental results indicate that compared with four assembly techniques, MIVAM-BHM-UFF shows better user experiences in the virtual assembly processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".