Acquiring a broad range of empirical knowledge in real time by temporal-difference learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several robot capabilities rely on predictions about the temporally extended consequences of a robot's behaviour. We describe how a robot can both learn and make many such predictions in real time using a standard algorithm. Our experiments show that a mobile robot can learn and make thousands of accurate predictions at 10 Hz. The predictions are about the future of all of the robot's sensors and many internal state variables at multiple time-scales. All the predictions share a single set of features and learning parameters. We demonstrate the generality of this method with an application to a different platform, a robot arm operating at 50 Hz. Here, learned predictions can be used to measurably improve the user interface. The temporally extended predictions learned in real time by this method constitute a basic form of knowledge about the dynamics of the robot's interaction with the environment. We also show how this method can be extended to express more general forms of knowledge.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it