---
pattern_id: pattern_end_to_end_model_based_learning
applicable_symptoms: [end_to_end_model_based_learning]
domain: Planning_Decision
---

# Cascaded perception-planning-control pipelines suffer from error propagation and lack of adaptability to new environments.

**Domain**: `Planning_Decision`

## Fix

Unify perception, planning, and control into a single differentiable computation graph with a learned model that can be fine-tuned via backpropagation.

## Anti-pattern

Pure model-based approaches with hand-designed dynamics or cost functions, and pure end-to-end black-box methods lacking interpretability.

## Cross-domain analogies

- **Perception_Vision** → Use a shared top-down latent representation to unify perception and planning, reducing cascaded errors.
  - related fix: Project sensory data into a bird's-eye-view grid representation, distilled from visual foundation models, to enable structured spatial reasoning for planning.
- **Learning_Training** → Train planning with offline data then online RL to adapt to new environments.
  - related fix: Two-stage training: first supervised fine-tuning on expert demonstrations, then reinforcement fine-tuning with policy gradient (e.g., PPO) to maximize task completion reward
- **Control_Locomotion** → Train an end-to-end policy via RL with domain randomization to replace cascaded modules.
  - related fix: Train a single end-to-end neural network policy via large-scale RL in simulation with domain randomization, mapping depth image directly to motor commands

## Patch

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

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

 # Symptom: Cascaded perception-planning-control pipelines suffer from error propagation and lack of adaptability to new environments.

+# Fix    : Unify perception, planning, and control into a single differentiable computation graph with a learned model that can be fine-tuned via backpropagation.

+# Avoid  : Pure model-based approaches with hand-designed dynamics or cost functions, and pure end-to-end black-box methods lacking interpretability.

```
