A grasp-based passivity signature for haptics-enabled human-robot interaction: Application to design of a new safety mechanism for robotic rehabilitation
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the biomechanical capability of the human upper limb in absorbing physical interaction energy during human-robot interaction is analyzed. The outcome is a graphical map that can quantitatively correlate the extent of the grasp pressure and the geometry of interaction to the extent of hand passivity. For this purpose, a user study has been conducted for 11 healthy human subjects to characterize the energy absorption capability in their arm and wrist. The above correlation is statistically validated. The identified user-specific grasp-based passivity signature map can be used as a graphical tool to assess the biomechanical capabilities of the upper limb in absorbing interaction energy. In this paper, the proposed grasp-based passivity signature map is utilized in the design of a new stabilizer for haptic systems, that takes into account the variation in energy absorption during haptic task execution. The goal is to optimize the haptic system fidelity while guaranteeing human-robot interaction stability despite the potential existence of delays and a non-passive environment. The controller is termed grasp-based passivity signature map stabilizer. If the user provides minimum to no energy absorption during the interaction, the controller makes the force reflection gate tight to guarantee stability. However, when the user demonstrates high capability in absorbing interaction energy, the controller allows the forces to be reflected. The grasp-based passivity signature map stabilizer is an alternative for both conventional stabilizers of haptic/telerobotic systems and fixed conservative force limits in rehabilitation systems where patient-robot interaction safety is a crucial requirement. This provides the practical motivation for this work. Experimental results are presented.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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 it