---
pattern_id: pattern_multitask_learning_for_vln_rxr_r2r
applicable_symptoms: [multitask_learning_for_vln_rxr_r2r]
domain: Learning_Training
---

# VLN models trained on a single dataset (e.g., R2R or RxR) fail to generalize across different instruction styles and environments, leading to poor navigation performance on unseen data.

**Domain**: `Learning_Training`

## Fix

Jointly train the VLN model on multiple annotated datasets (RxR and R2R) using multitask learning to learn shared visual and linguistic representations, improving robustness and generalization.

## Anti-pattern

Training on a single dataset (e.g., only R2R or only RxR) results in overfitting to dataset-specific patterns and limited cross-dataset generalization.

## Cross-domain analogies

- **Perception_Vision** → Use a fixed-size latent instruction encoder to compress diverse style inputs via cross-attention.
  - related fix: Use a cross-attention bottleneck: project arbitrary input to a fixed-size latent array via cross-attention, then process with iterative self-attention in latent space.
- **Planning_Decision** → Use cross-modal attention to align imagined instruction styles with observed environments.
  - related fix: Train a visual imagination module that predicts future visual observations conditioned on language instructions and current visual input, then integrate imagined features into the navigation policy via cross-modal attention.
- **Control_Locomotion** → Train end-to-end policy with large-scale domain randomization over instructions and environments.
  - 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
--- multitask_learning_for_vln_rxr_r2r.before.py
+++ multitask_learning_for_vln_rxr_r2r.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

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

 # Symptom: VLN models trained on a single dataset (e.g., R2R or RxR) fail to generalize across different instruction styles and environments, leading to poor navigation performance on unseen data.

+# Fix    : Jointly train the VLN model on multiple annotated datasets (RxR and R2R) using multitask learning to learn shared visual and linguistic representations, improving robustness and generalization.

+# Avoid  : Training on a single dataset (e.g., only R2R or only RxR) results in overfitting to dataset-specific patterns and limited cross-dataset generalization.

```
