Bayesian Optimization with Safety Constraints: Safe and Automatic\n Parameter Tuning in Robotics
Bibliographic record
Abstract
Robotic algorithms typically depend on various parameters, the choice of\nwhich significantly affects the robot's performance. While an initial guess for\nthe parameters may be obtained from dynamic models of the robot, parameters are\nusually tuned manually on the real system to achieve the best performance.\nOptimization algorithms, such as Bayesian optimization, have been used to\nautomate this process. However, these methods may evaluate unsafe parameters\nduring the optimization process that lead to safety-critical system failures.\nRecently, a safe Bayesian optimization algorithm, called SafeOpt, has been\ndeveloped, which guarantees that the performance of the system never falls\nbelow a critical value; that is, safety is defined based on the performance\nfunction. However, coupling performance and safety is often not desirable in\nrobotics. For example, high-gain controllers might achieve low average tracking\nerror (performance), but can overshoot and violate input constraints. In this\npaper, we present a generalized algorithm that allows for multiple safety\nconstraints separate from the objective. Given an initial set of safe\nparameters, the algorithm maximizes performance but only evaluates parameters\nthat satisfy safety for all constraints with high probability. To this end, it\ncarefully explores the parameter space by exploiting regularity assumptions in\nterms of a Gaussian process prior. Moreover, we show how context variables can\nbe used to safely transfer knowledge to new situations and tasks. We provide a\ntheoretical analysis and demonstrate that the proposed algorithm enables fast,\nautomatic, and safe optimization of tuning parameters in experiments on a\nquadrotor vehicle.\n
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".