Human-Aware Reinforcement Learning for Fault Recovery Using Contextual Gaussian Processes
Bibliographic record
Abstract
This work addresses the iterated nonstationary assistant selection problem, in which over the course of repeated interactions on a mission, an autonomous robot experiencing a fault must select a single human from among a group of assistants to restore it to operation. The assistants in our problem have a level of performance that changes as a function of their experience solving the problem. Our approach uses reinforcement learning via a multi-arm bandit formulation to learn about the capabilities of each potential human assistant and decide which human to task. This study, which is built on our past work, evaluates the potential for a Gaussian-process-based machine learning method to effectively model the complex dynamics associated with human learning and forgetting. Application of our method in simulation shows that our method is capable of tracking performance of human-like dynamics for learning and forgetting. Using a novel selection policy called the proficiency window, it is shown that our technique can outperform baseline selection strategies while providing guarantees on human use. Our work offers an effective potential alternative to dedicated human supervisors, with application to any human–robot system where a set of humans is responsible for overseeing autonomous robot operations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".