arXiv: cs.AI preprintsInternational6 November 2026
Generate-then-Rank: Learning Symbolic Goal Interpretation for Embodied Agents
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arXiv:2611.07825v1 Announce Type: new Abstract: Goal interpretation is to translate a natural language instruction into a formal symbolic goal that a planner can execute. It is critical for embodied agents because any error here propagates to every downstream planning and action step. Doing it well requires both generating suitable candidates and reliably selecting among them. We present a two-stage generate-then-rank approach to Goal Interpretation in the Embodied Agent Interface (EAI). First, supervised fine-tuning adapts a Large Language Model (LLM) to EAI's output convention. Second, Candi
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