どんな家でもどんなタスクでも：抽象的ヒューマンタスクのためのスケーラブルな長期計画
Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
大規模家庭環境での曖昧な指示に対応するため、LLMで指示とシーングラフをPDDLサブゴールに変換し、記号推論で長期計画を生成する手法AHATを提案。中間推論の外部修正をGRPOに統合したTGPOで性能を向上。
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著者: Zhihong Liu, Yang Li, Rengming Huang, Cewu Lu, Panpan Cai
分類: cs.RO
原文アブストラクト
Open world language conditioned task planning is crucial for robots operating in large-scale household environments. While many recent works attempt to address this problem using Large Language Models (LLMs) via prompting or training, a key challenge remains scalability. Performance often degrades rapidly with increasing environment size, plan length, instruction ambiguity, and constraint complexity. In this work, we propose Any House Any Task (AHAT), a household task planner optimized for long-horizon planning in large environments given ambiguous human instructions. At its core, AHAT utilizes an LLM trained to map task instructions and textual scene graphs into grounded subgoals defined in the Planning Domain Definition Language (PDDL). These subgoals are subsequently solved to generate feasible and optimal long-horizon plans through explicit symbolic reasoning. To enhance the model's ability to decompose complex and ambiguous intentions, we introduce TGPO, a novel reinforcement learning algorithm that integrates external correction of intermediate reasoning traces into Group Relative Policy Optimization (GRPO). Experiments demonstrate that AHAT achieves significant performance gains over state-of-the-art prompting, planning, and learning methods, particularly in human-style household tasks characterized by brief instructions but requiring complex execution plans.
