Skip to content

world-models.md

Latest commit

 

History

History
92 lines (72 loc) · 3.36 KB

world-models.md

File metadata and controls

92 lines (72 loc) · 3.36 KB
tags
broomva
control-kernel
architecture
type spec
status active
area world-model
created 2026-03-17

World Models

Learned dynamics, digital twins, and data-driven prediction methods that serve as the "plant model" for MPC-style controllers.

Koopman Methods

Lift nonlinear dynamics into higher-dimensional observable space where linear predictors enable efficient MPC.

Agent Integration

  • Artifact: Koopman lift function (EDMD variants) — updatable via EGRI
  • Controller: Koopman-MPC using lifted linear system
  • LLM role: Decide when to relearn lifts, curate datasets, interpret model mismatch indicators, pick robust strategies
  • Verification: Error bounds on approximation, closed-loop stability checks

When to Use

  • System has underlying structure that linearizes in lifted coordinates
  • Computational budget allows offline lift learning
  • Need fast online MPC (linear system → fast QP)

Data-Driven MPC / DeePC

Uses input/output trajectory data (Hankel matrices) for prediction and optimization without an explicit parametric model.

Agent Integration

  • Harness: Dataset store + experiment runner (collect trajectories)
  • Controller: DeePC optimizer (QP/convex program) as a tool
  • LLM role: Choose excitation experiments, select horizons/regularization, interpret results, update constraints
  • Safety: Wrap DeePC output with CBF-QP shield or robust constraint tightening

Robust DeePC

Regularized and distributionally robust formulations interpret regularization as DRO and provide probabilistic robustness guarantees. Use when data is noisy or distribution shifts are expected.

Digital Twins

Real-time virtual replicas supporting monitoring, simulation, prediction, optimization.

Agent Integration

  • Role: Provides the harness for safe experimentation and scenario evaluation
  • EGRI integration: Twin is the execution backend for controller improvement trials
  • LLM role: Orchestrate simulation experiments, interpret mismatches between twin predictions and real plant observations
  • Calibration: Twin validity metrics are a first-class evaluator input

Digital Twin as EGRI Backend

# In problem-spec.yaml
execution:
  backend: simulator
  command: "python3 twin/run_scenario.py --config {{scenario}}"
  timeout_s: 60
  sandbox: true  # Twin runs are inherently sandboxed

Learned Dynamics (Neural, GP, etc.)

Model-based approaches that learn environment models from data.

Agent Integration

  • Artifact: Model weights/parameters — improved via EGRI loops
  • Controller: Model-based MPC using learned predictions
  • LLM role: Select model architectures, design training harnesses, interpret training curves, gate deployment
  • Safety: Prediction uncertainty estimates feed into robust MPC constraints

Method Selection Guide

Scenario Recommended Reasoning
Linear/weakly nonlinear, fast MPC needed Koopman + MPC Fast QP solves, good approximation
No parametric model, good data available DeePC Model-free, direct from data
Complex nonlinear, simulation available Digital twin + EGRI Safe offline optimization
Distribution shift expected Robust DeePC or DRO-MPC Built-in robustness guarantees
Cyber plant (code/workflows) Learned heuristics + EGRI LLM curates, evaluator judges