Uncertainty-Aware Policy Sampling and Mixing for Safe Interactive Imitation Learning
Bibliographic record
Abstract
Teaching robots how to execute tasks through demonstrations is appealing since it sidesteps the need to explicitly specify a reward function. However, posing imitation learning as a simple supervised learning problem suffers from the well-known problem of distributional shift - the teacher will only demonstrate the optimal trajectory and therefore the learner is unable to recover if it deviates even slightly from this trajectory since it has no training data for this case. This problem has been overcome in the literature by some element of interactivity in the learning process - usually be somehow interleaving the execution of the learner and the teacher so that the teacher can demonstrate to the learner also how to recover from mistakes. In this paper, we consider the cases where the robot has the potential to do harm, and therefore safety must be imposed at every step in the learning process. We show that uncertainty is an appropriate measure of safety and that both the mixing of the policies and the data sampling procedure benefit from considering the uncertainty of both the learner and the teacher. Our method, uncertainty-aware policy sampling and mixing (UPMS), is used to teach an agent to drive down a lane with less safety violations and less queries to the teacher than state-of-the-art methods.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".