On the performance evaluation of a vision-based human-robot interaction framework
Bibliographic record
Abstract
This paper describes the performance evaluation of a machine vision-based human-robot interaction framework, particularly those involving human-interface studies. We describe a visual programming language called RoboChat, and a complimentary dialog engine which evaluates the need for confirmation based on utility and risk. Together, RoboChat and the dialog mechanism enable a human operator to send a series of complex instructions to a robot, with the assurance of confirmations in case of high task-cost or command uncertainty, or both. We have performed extensive human-interface studies to evaluate the usability of this framework, both in controlled laboratory conditions and in a variety of outdoors environments. One specific goal for the RoboChat scheme was to aid a scuba diver to operate and program an underwater robot in a variety of deployment scenarios, and the real-world validations were thus performed on-board the Aqua amphibious robot [4], in both underwater and terrestrial environments. The paper describes the details of the visual human-robot interaction framework, with an emphasis on the RoboChat language and the confirmation system, and presents a summary of the set of performance evaluation experiments performed both on- and off-board the Aqua vehicle.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".