Bibliographic record
Abstract
Skill acquisition is among the most remarkable aspects of human intelligence. It involves discovering purposeful behavioural modules, retaining them as skills, honing them through practice, and applying them in unforeseen circumstances [11]. Skill acquisition underlies our ability to choose to spend time and energy on the mastery of particular tasks and draw upon previous experience to solve more complex problems over time with less cognitive effort[10]. If endowed with continual skill acquisition, robots can autonomously improve their skills over time, where learning at one stage of development is a foundation for future learning [23]. It could unlock new possibilities for physical automation with general-purpose robots, just as general-purpose computer processors ushered in the information age [24, 33]. In this work, we propose a novel approach called Graduated Learning, where we ask a robot to acquire new manipulation and locomotion skills repeatedly, using time-delineated experiences of attempts at those skills (i.e., episodes) and some store of previously acquired knowledge (e.g., weights of a neural network). Our proposed approach chooses the order in which an agent learns these skills since the progressive manner in which they are developed plays a vital role in developing a final skill set.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".