言語モデルをゼロショットプランナーとして活用：身体性エージェントのための実行可能知識の抽出
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
大規模言語モデルが追加学習なしに高レベルな自然言語タスクを中間計画に分解できることを示し、既存の実演に基づいて計画を実行可能な行動に変換する手法を提案した論文。
著者: Wenlong Huang, Pieter Abbeel, Deepak Pathak, Igor Mordatch
分類: cs.LG, cs.AI, cs.CL, cs.CV, cs.RO
原文アブストラクト
Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. "make breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Website at https://huangwl18.github.io/language-planner
