zjunlp/alfworld-goal-interpreter

Goal Interpretation Protocol

1. Parse the Task Statement

When a new task is assigned, immediately analyze the natural language instruction to extract its core components. Use the parsing script (parse_goal.py) to perform this analysis.

Input: The raw task string (e.g., "look at pillow under the desklamp"). Output: A structured dictionary containing:

• primary_target: The main object to interact with.
• reference_object: The object that defines a location or condition.
• spatial_relation: The preposition linking them (e.g., under, on, in).
• action: The verb defining the required interaction.

2. Generate Sub-Objectives

Based on the parsed components, formulate a clear, sequential plan. The plan must account for the spatial relationship.

Example Logic:

• IF relation is under → Sub-goal 1: Locate the reference_object. Sub-goal 2: Inspect the area beneath it for the primary_target.
• IF relation is in or on → Sub-goal 1: Locate the reference_object. Sub-goal 2: Check its contents/surface.

3. Identify Required Objects & Actions

Map the parsed objects to the available action types in the environment. The primary action verbs (look, take, use, etc.) from the task must be translated into the agent's available action set (go to, take, use, etc.).

Critical Check: If the reference_object is a container (drawer, fridge), ensure the plan includes the open action before inspection.

4. Execute & Adapt

Initiate the plan. After each action, monitor the observation. If the expected object is not found, or the action fails ("Nothing happened"), consult the fallback logic in the reference guide (search_patterns.md).

Remember: The agent must maintain object permanence. If an object is moved (e.g., a pillow is picked up), it is now in the agent's inventory and the spatial relationship is void.