Tortoise and the Hare Robot: Slow and steady almost wins the race, but finishes more safely
Bibliographic record
Abstract
We investigated the effects of changing the tele-operation feel of operating a robot by modifying its speed and acceleration profiles, and found that reducing a robot's maximum speed by half can reduce collisions by 32%, while only increasing navigation task time by 10%. Teleoperated robots are increasingly popular for enabling people to remotely attend meetings, explore dangerous areas, or view tourist destinations. As these robots are being designed to work in crowded areas with people, obstacles, or even unpredictable debris, interfaces that support piloting them in a safe and controlled manner are important for successful teleoperation. We investigate modifying a teleoperated robot's speed and acceleration profiles on an operator remotely navigating through an obstacle course. Our results indicate that lower maximum speeds result in lower operator workload, fewer collisions, and are only slightly slower than other profiles with a higher maximum speed. Our results raise questions about how robot designers should think about physical robot capability design and default driving software settings, the robot control interface, and the relation of robot speed to control.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".