taomiao/dynamicskillcompiler/alfworld-search-pattern-executor

Skill: Systematic Object Search

Purpose

Use this skill when you need to find a specific object in a household environment without prior knowledge of its exact location. The skill implements a robust search pattern based on common sense about where objects are typically stored.

Core Workflow

1. Input: A target object name (e.g., remotecontrol) and an ordered list of candidate receptacles to search.
2. Process: Navigate to each candidate location in sequence. For each receptacle:

• If it's closed, open it.
• Inspect its contents.
• If the target object is found, take it and proceed to the placement phase.
• If the receptacle was opened and is empty, close it before moving on.

1. Output: Success when the object is found, or a failure state after all candidates are exhausted.

Key Principles

• Methodical Search: Do not skip locations in the provided sequence unless the object is found.
• State Management: Always close drawers/cabinets after checking them if they were opened.
• Focus: Once the object is found, immediately transition to the next phase of the task (e.g., put). Avoid redundant searches.
• Error Handling: If an action fails (e.g., "Nothing happened"), the skill logic in scripts/search_orchestrator.py provides fallback reasoning.

Usage Example

Goal: "find two remotecontrol and put them in armchair." Skill Execution:

1. Activate this skill with target=remotecontrol, candidates=['sofa 1', 'sidetable 1', 'dresser 1', 'drawer 1', 'drawer 2', 'drawer 3', 'drawer 4', 'coffeetable 1'].
2. The skill searches locations in order. It finds the first remotecontrol on coffeetable 1.
3. The agent takes the object and proceeds to place it in armchair 1.
4. The skill is re-activated for the second remotecontrol, searching the remaining candidates (or the same list). It finds the second remotecontrol on coffeetable 1.
5. The agent takes and places the second object, completing the task.

Integration

This skill is designed to be called as a subroutine within a larger task plan. The bundled search_orchestrator.py script handles the low-level action sequencing to prevent logical errors and repetitive mistakes observed in the learning trajectory.