Grip forces and load forces in handovers
Bibliographic record
Abstract
In this study, we investigate and characterize haptic interaction in human-to-human handovers and identify key features that facilitate safe and efficient object transfer. Eighteen participants worked in pairs and transferred weighted objects to each other while we measured their grip forces and load forces. Our data show that during object transfer, both the giver and receiver employ a similar strategy for controlling their grip forces in response to changes in load forces. In addition, an implicit social contract appears to exist in which the giver is responsible for ensuring object safety in the handover and the receiver is responsible for maintaining the efficiency of the handover. Compared with prior studies, our analysis of experimental data show that there are important differences between the strategies used by humans for both picking up/placing objects on table and that used for handing over objects, indicating the need for specific robot handover strategies as well. The results of this study will be used to develop a controller for enabling robots to perform object handovers with humans safely, efficiently, and intuitively.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".