Optimal Scheduling of Models and Horizons for Model Hierarchy Predictive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Model predictive control (MPC) is a powerful tool to control systems with non-linear dynamics and constraints, but its computational demands impose limitations on the dynamics model used for planning. Instead of using a single complex model along the MPC horizon, model hierarchy predictive control (MHPC) reduces solve times by planning over a sequence of models of varying complexity within a single horizon. Choosing this model sequence can become intractable when considering all possible combinations of reduced order models and prediction horizons. We propose a framework to systematically optimize a model schedule for MHPC. We leverage trajectory optimization (TO) to approximate the accumulated cost of the closed-loop controller. We trade off performance and solve times by minimizing the number of decision variables of the MHPC problem along the horizon while keeping the approximate closed-loop cost near optimal. The framework is validated in simulation with a planar humanoid robot as a proof of concept. We find that the approximated closed-loop cost matches the simulated one for most of the model schedules, and show that the proposed approach finds optimal model schedules that transfer directly to simulation, and with total horizons that vary between 1.1 and 1.6 walking steps.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it