Tap and push: assessing the value of direct physical control in human-robot collaborative tasks
Bibliographic record
Abstract
In this paper, we compare a touch-based human-to-robot command scheme with traditional button commands in a series of human-robot collaborative assembly tasks. We find a mapping between command style and task outcome that depends on task complexity and is influenced by robot feel. In our direct touch-based scheme, the user commands the robot through direct physical contact by tapping and pushing the robot. With a small, compliant desktop robot and a simple, scripted, bolt insertion task, button commands performed slightly better than direct physical commands in quantitative task performance metrics and qualitative user preference. In a second study with a human-scale, stiffer robot arm, physical commands performed better than button commands in a more complex and less scripted bolt insertion task, which greatly outperformed using buttons in a cooperative positioning task. We conclude that commanding a robot through direct force-transmitting contact can decrease task completion time, aid in teamwork, and improve user experience in appropriately chosen tasks. We achieve our haptic commands using only robot position sensors, demonstrating that direct, intuitive physical command is an option for existing position-controlled industrial robots.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".