Haptic Interface for Handshake Emulation
Bibliographic record
Abstract
This letter introduces a prototype of a haptic interface designed to produce a realistic human-robot handshake. Inspired by the human hand anatomy, a new robotic hand designed to achieve a realistic palm compliance and finger grasping is presented. As the system is backdrivable, a position-controlled feedback loop is implemented to render a human-like hand behavior. The overall arm motion is achieved through a collaborative serial manipulator. This manipulator uses an impedance control around a sinusoidal trajectory to simulate its intention or personality. Improved from the design proposed by the authors in previous work, the new prototype is easier to use, more efficient, more robust, and more comfortable with an active arm behavior. Experiments are then performed to determine the impact of different trajectory parameters, such as frequency, amplitude, and damping and stiffness coefficients, on the perceived realism of the handshake. It is shown that the amplitude has no impact in the range studied (10 to 30mm), while a frequency of approximately 2 Hz is preferred. Ranges of values of the damping and stiffness coefficients yielding the best results are also determined. The experiments also allow the identification of potential improvements to be implemented on the prototype in the future.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".