A qualitative evaluation criterion for human-robot interaction system in achieving collective tasks
Bibliographic record
Abstract
This work intends to identify common performance metrics for task-oriented human-robot interaction. We present a methodology to assess the system performance of a human-robot team in achievement of collective tasks. We propose a systematic approach that addresses the performance of both the human user and the robotic agent as a team. Toward this end, we attempt to determine the true time that an operator has to dedicate to a robot in action. We define the robot attention demand (RAD) as a function of both direct interaction time (DIT) and indirect interaction time (IIT), where the IIT is a direct consequence of the human trust in automation. We propose a two-level fuzzy temporal model to evaluate the human trust in automation while interacting with robots. Another fuzzy temporal model is presented to evaluate the human reliability during interaction time. The model is then generalized to accommodate multi-robot scenarios. Sequential and parallel robot cooperation schemes with varying levels of task dependency are considered. The fuzzy knowledge bases are further updated by implementing an application robotic platform where robots and users interact naturally to complete tasks with varying levels of complexity. User feedback is noted and used to tune the knowledge base rules where needed, to better represent a human expert's knowledge.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".