J-Strips: Haptic Joint Limit Warnings for Human-Robot Interaction
Bibliographic record
Abstract
The capabilities of humans and robots naturally compliment each other. Humans excel at spatial problem solving and fine manipulation tasks, whereas robots are good at supporting and stabilizing heavy loads. However, the typical impedance control strategy employed in this domain does not communicate any of a robot’s underlying physical constraints to its user. In this work, we propose j-strips, an anthromimetic haptic cue designed to convey stress in a robot manipulator to its human user. We hypothesize that when warned via j-strips that the robot is nearing a joint limit in this way, the human user will modify the path of the robot to avoid the limit. We present the results of a pilot study of three human subjects manipulating a robot arm that uses j-strips to warn its user when its elbow position limit is approached. Two of the subjects were found to significantly modify the manner in which they manipulated the robot, and both verbally reported that j-strips conveyed the intended message.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".