---
pattern_id: pattern_learned_perceptive_forward_dynamics_model_for_safe_and_platform_aware_robotic_na
applicable_symptoms: [learned_perceptive_forward_dynamics_model_for_safe_and_platform_aware_robotic_na]
domain: Planning_Decision
---

# Mobile robots navigating in unstructured environments often fail due to unmodeled terrain dynamics and platform-specific constraints, leading to collisions or instability.

**Domain**: `Planning_Decision`

## Fix

Learn a perceptive forward dynamics model that predicts future states from visual observations and robot state, then use it in a model predictive control framework for safe, platform-aware navigation.

## Anti-pattern

Traditional geometric or kinematic models that ignore terrain interaction and platform dynamics.

## Cross-domain analogies

- **Perception_Vision** → Hierarchical open-vocabulary graph mapping enables adaptive terrain classification and constraint-aware path planning.
  - related fix: Open-vocabulary hierarchical 3D graph representation combining VLM-based instance/caption extraction from images, incremental LiDAR projection, and lane graph connectivity segmentation.
- **Learning_Training** → Bootstrap with offline terrain data, then refine online with adaptive RL.
  - related fix: Mixed Imitation and Reinforcement Learning (MIRL): bootstrap policy via off-policy imitation learning, then refine with on-policy RL, gradually shifting weight from imitation to RL.
- **Control_Locomotion** → Use visual-motor closed-loop mapping to adapt navigation plans to real-time terrain dynamics.
  - related fix: Train a visual locomotion policy that maps camera images and proprioception to joint actions, enabling real-time foot placement adaptation to obstacles.

## Patch

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

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

 # Symptom: Mobile robots navigating in unstructured environments often fail due to unmodeled terrain dynamics and platform-specific constraints, leading to collisions or instability.

+# Fix    : Learn a perceptive forward dynamics model that predicts future states from visual observations and robot state, then use it in a model predictive control framework for safe, platform-aware navigation.

+# Avoid  : Traditional geometric or kinematic models that ignore terrain interaction and platform dynamics.

```
