Experience-Based Model Selection to Enable Long-Term, Safe Control for Repetitive Tasks Under Changing Conditions
Bibliographic record
Abstract
Learning approaches have enabled significant performance improvements in robotic control allowing robots to execute motions that were previously impossible. The majority of the work to date, however, assumes that the parts to be learned are static or slowly changing, which limits their applicability in realistic scenarios with rapid changes in the conditions. This paper presents a method to extend an existing single-mode safe learning controller based on Gaussian Process Regression to learn an increasing number of non-linear models for the robot dynamics. We show that this approach enables a robot to re-use past experiences from a large number of previously visited operating conditions, and to safely adapt when a new and distinct operating condition is encountered. This allows the robot to achieve safety and high performance in a large number of operating conditions that do not have to be specified ahead of time. Our approach runs independently from the controller, imposing no additional computation time on the control loop regardless of the number of previous operating conditions considered. We demonstrate the effectiveness of our approach in experiment on a 900 kg ground robot with both physical and artificial changes to its dynamics. All of our experiments are conducted using vision for localization.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".