Natural Human–Robot Interface Using Adaptive Tracking System with the Unscented Kalman Filter
Bibliographic record
Abstract
Traditional human-robot interfaces usually have limitations in accuracy and/or operational space. This article proposes a natural human-robot interface using an adaptive tracking method, which can effectively expand the operational space while ensuring high accuracy. The natural interface allows the robot to directly reproduce the user's hand movement, making the interaction more intuitive and natural. The leap motion is fixed on the Cartesian platform to capture the movement of the user's hand. Because the Cartesian platform follows the hand and keeps the hand in the center of the detection area, the measurement accuracy is improved and the measurement space can be extended. During the process of acquiring gesture data, the measurement errors were found to increase over time because of the inherent noise of the sensor. To deal with this problem, the unscented Kalman filter is applied to estimate the position of the hand. Moreover, an adaptive velocity control method is proposed to improve the operation accuracy and reduce the task execution time with the consideration of users' habits and easiness of usage. The effectiveness of this interface is verified by a series of experiments, and the results show that the proposed interface can be used by nonprofessional users for object operation tasks and can provide users with superior interactive experiences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".