---
pattern_id: pattern_alfred_a_benchmark_for_interpreting_grounded_instructions_for_everyday_tasks
applicable_symptoms: [alfred_a_benchmark_for_interpreting_grounded_instructions_for_everyday_tasks]
domain: Planning_Decision
---

# Embodied agents fail to follow long, grounded natural language instructions in household tasks due to lack of fine-grained action and object grounding.

**Domain**: `Planning_Decision`

## Fix

ALFRED benchmark: decompose tasks into subgoals with expert demonstrations and evaluate agents on step-by-step action prediction and object interaction.

## Anti-pattern

Prior benchmarks lacked fine-grained action sequences and object grounding for everyday tasks.

## Cross-domain analogies

- **Perception_Vision** → Apply Laplacian variance filtering to stabilize grounding by pre-screening action-object relevance.
  - related fix: Apply Laplacian Variance Filtering to stabilize camera feed before detection.
- **Learning_Training** → Parallelize grounded subgoal verification across multiple local planners with synchronized feedback.
  - related fix: Distribute PPO training across multiple workers with synchronized gradient updates (DD-PPO).
- **Control_Locomotion** → Incorporate perceptual grounding into the policy to dynamically adapt actions and object interactions.
  - related fix: Incorporate terrain information (depth images or elevation maps) into the control policy to dynamically adapt gait, foothold placement, and body posture.

## Patch

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

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

 # Symptom: Embodied agents fail to follow long, grounded natural language instructions in household tasks due to lack of fine-grained action and object grounding.

+# Fix    : ALFRED benchmark: decompose tasks into subgoals with expert demonstrations and evaluate agents on step-by-step action prediction and object interaction.

+# Avoid  : Prior benchmarks lacked fine-grained action sequences and object grounding for everyday tasks.

```
