Generate-then-Rank: Learning Symbolic Goal Interpretation for Embodied Agents

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, Candidate-Output Ranking from Errors (CORE) learns preferences over complete outputs from the model's own generated alternatives. We conducted experiments on the household scenes of the Embodied Agent Interface Benchmark. Multiple open source LLMs including DeepSeek-V3 chat, DeepSeek-Reasoner, and Qwen2.5-7B and Qwen2.5-72B were evaluated. The proposed method outperformed the baseline models including LLMs with simple prompting, chain-of-thought, and knowledge graph based approaches.

14 pages, 2 figures. Accepted at the 10th Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2026)

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)

arXiv:2611.07825 [cs.AI]

(or arXiv:2611.07825v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07825 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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