Tap and push: assessing the value of direct physical control in human-robot collaborative tasks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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