A Bayesian Method for Learning POMDP Observation Parameters for Robot Interaction Management Systems
Bibliographic record
Abstract
Technology has allowed robots to enter more personal settings in our society, appearing in environments alongside humans. These new situations provide a new set of problems, including the interaction and control of the robot by untrained humans, as well as adapting to an unconstrained world designed for humans. In this paper, we address the issue of robot learning in these environments while taking advantage of a user working alongside the robot. We present a framework for gradually learning a model of the user through a parametric observation function. This type of framework allows us to begin with a rough model of the world and adjust it from experience. By relying on an oracle providing optimal policy information, we are able to learn the observation model and adjust the robot’s behavior to match that of the oracle. We address the problems of learning and modifications necessary to handle the observation function and learning for rare events. We demonstrate the feasibilty of the algorithm on a robot-interaction domain and compare against a model-free method for action-selection.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".