Transfer Learning for Robotics: Can a Robot Learn from Another Robot's Data?
Bibliographic record
Abstract
Machine learning methods have been used to improve the performance of control systems with experimental data when accurate system or environment models are unavailable. Transfer Learning (TL) allows for this data to come from another, similar system. A simplified TL scenario is studied to understand how the quality of an alignment-based transfer of data varies with the parameters of two linear, time-invariant (LTI), single-input, single-output systems that are tasked to follow the same reference signal. A scalar, LTI transformation is used to align the output from a source system to the output from a target system. An upper bound on the 2-norm of the transformation error is derived and minimized with respect to the transformation scalar. This minimized bound is analyzed with respect to the system parameters to show when TL works best. TL is further studied for wheeled robots, with data from simulation and experiment to supplement theoretical analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".