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alfworld-goal-interpreter【开发与工程】Parses the natural language task goal to extract actionable sub-objectives and required objects. Trigger this skill whenever a new task is assigned to break down complex instructions into clear, sequential targets. It interprets phrases like 'look at X under Y' to identify target objects (pillow), reference objects (desklamp), and spatial relationshi…
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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.Based on the parsed components, formulate a clear, sequential plan. The plan must account for the spatial relationship.
Example Logic:
under → Sub-goal 1: Locate the reference_object. Sub-goal 2: Inspect the area beneath it for the primary_target.in or on → Sub-goal 1: Locate the reference_object. Sub-goal 2: Check its contents/surface.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.
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.