An Experimental Evaluation of Bayesian Soft Human Sensor Fusion in Robotic Systems
Bibliographic record
Abstract
As we move forward into the twenty-rst century, we are seeing new horizons opening to us through the use of autonomous robots as explorers, going places we can’t or won’t. The interaction between the human operator and robotic agent is of paramount importance in exploring these new horizons. Both humans and robots have their strengths and weaknesses, and this paper explores how they compliment each other and looks at new, more ecient ways to facilitate communication between the robot and its operator. This paper investigates conditions to the use of Bayesian information fusion algorithms with Gaussian mixture models to fuse soft human input with robotic sensor data to complete cooperative mission objectives. The condition investigated here are the benets of training individual models for the human input, or if a generic model trained on many people is sucient to complete the mission; we are also seeking to determine if it is useful to allow the human operator to assign a condence value to the information sent to the robot. These two conditions are evaluated with sizable number of human subjects working with a robotic platform to complete a multi-target search mission. Along with the results of these two conditions, reactions to the limited interaction with a robotic partner of a variety of people will be presented based on a survey that was performed once the trials were completed.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".