Conformal Distributionally-Robust Model Predictive Control with a Budgeted Multi-Step Barrier
Bibliographic record
Abstract
Ensuring safety under model uncertainty and distribution shift remains a central challenge for learning-enhanced model predictive control (MPC). In this study, Conformal Distributionally-Robust MPC with a Budgeted Barrier (CDRMPC) is introduced as a planner-first framework that combines (i) conformal residual calibration for finite-sample coverage, (ii) Wasserstein distributional robustness to hedge against distribution shift, and (iii) a horizon-coupled, budgeted multi-step barrier (BMB) that allocates safety slack over time rather than relying on myopic enforcement. The conformal-DR residual set adapts online to the data stream, and the BMB couples barrier slacks across the horizon via a weighted budget that is optimized and penalized in the MPC. Tractable formulations are obtained using linearization and norm-bounded reachableset over-approximations, probabilistic feasibility arguments are sketched, and validation is performed on an integrator and an inverted pendulum under parametric drift and injected bias. Compared to fixed-tube MPC, chance-constrained MPC, and adaptive-tube+CBF-QP shielding, CDR-MPC reduces safety overrides and violations at comparable control cost.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".