Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it