---
pattern_id: pattern_atsushisakaipyadvancedcontrol
applicable_symptoms: [atsushisakaipyadvancedcontrol]
domain: Control_Locomotion
---

# MPC optimization with state constraints is computationally expensive when using high-level modeling tools like cvxpy

**Domain**: `Control_Locomotion`

## Fix

Implement MPC optimization directly using a solver (cvxopt) with explicit constraint matrices, bypassing modeling tools

## Anti-pattern

Using cvxpy for MPC optimization modeling adds overhead and limits scalability

## Cross-domain analogies

- **Perception_Vision** → Use synthetic constraint distributions to pre-train a fast surrogate solver.
  - related fix: Generate synthetic depth images with simulated sensor noise, self-occlusion, and lighting effects to match real sensor distribution.
- **Planning_Decision** → Use hierarchical decomposition with auxiliary sub-tasks to reduce online MPC constraint complexity.
  - related fix: Use panoramic action space, progress monitoring, and pre-trained vision-language models (e.g., VLN-BERT) with auxiliary tasks like single-step reasoning and backtracking.
- **Learning_Training** → Use full-kinematics agents with a robust physics engine to reduce computational cost by precomputing feasible trajectories.
  - related fix: Use full-kinematics agents with a robust physics engine to enable realistic motion dynamics and high-fidelity simulation, reducing sim-to-real gap.

## Patch

```diff
--- atsushisakaipyadvancedcontrol.before.py
+++ atsushisakaipyadvancedcontrol.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: MPC optimization with state constraints is computationally expensive when using high-level modeling tools like cvxpy

+# Fix    : Implement MPC optimization directly using a solver (cvxopt) with explicit constraint matrices, bypassing modeling tools

+# Avoid  : Using cvxpy for MPC optimization modeling adds overhead and limits scalability

```
