Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer
Bibliographic record
Abstract
It has been established that the transfer of human adaptive impedance is of great significance for physical human-robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor's arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".