Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses
Bibliographic record
Abstract
We introduce a new method for robot control that combines prediction learning with a fixed, crafted response---the robot learns to make a temporally-extended prediction during its normal operation, and the prediction is used to select actions as part of a fixed behavioral response. Our method is inspired by Pavlovian conditioning experiments in which an animal's behavior adapts as it learns to predict an event. Surprisingly the animal's behavior changes even in the absence of any benefit to the animal (i.e. the animal is not modifying its behavior to maximize reward). Our method for robot control combines a fixed response with online prediction learning, thereby producing an adaptive behavior. This method is different from standard non-adaptive control methods and also from adaptive reward-maximizing control methods. We show that this method improves upon the performance of two reactive controls, with visible benefits within 2.5 minutes of real-time learning on the robot. In the first experiment, the robot turns off its motors when it predicts a future over-current condition, which reduces the time spent in unsafe over-current conditions and improves efficiency. In the second experiment, the robot starts to move when it predicts a human-issued request, which reduces the apparent latency of the human-robot interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".