Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives
Bibliographic record
Abstract
The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions. 1
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".