A Gesture-Based Natural Human–Robot Interaction Interface With Unrestricted Force Feedback
Bibliographic record
Abstract
This article presents a novel gesture-based natural human–robot interaction interface, which integrates a markerless gesture tracking system and an unrestricted electromagnetic force feedback mechanism. In this proposed interface, a markerless gesture tracking system is developed to relate the motion of the operator’s hand to the robot manipulator so that the operator can naturally and friendly control the robot without any markers. More importantly, this interface uses a new unrestricted electromagnetic force feedback mechanism to avoid the friction, hysteresis, and other nonlinear influence in the conventional actuation dynamics. The interface makes the operator obtain the effective force immersion of the robot. Therefore, the proposed interface can provide the promising operation accuracy for the operator. To effectively regulate the electric currents of coils and provide accurate force feedback, the broad learning system (BLS) is introduced in this unrestricted electromagnetic force feedback mechanism. In addition, two interval Kalman filters (IKFs) are applied to estimate the position and orientation of the operator’s hand, respectively, improving the measurement accuracy of the proposed interface. Experimental results show that the proposed interface is suitable for high-precision human–robot interactive tasks and enables the operator to focus on the tasks and operate dual robot manipulator, which provides natural and efficient human–robot interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".