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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".