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The paper introduces Any House Any Task (AHAT), a household task planner designed for long-horizon planning in large environments with ambiguous instructions. AHAT trains an LLM to map task instructions and textual scene graphs into PDDL subgoals, which are then solved using symbolic reasoning for optimal plan generation. To improve decomposition of complex intentions, they propose TGPO, a reinforcement learning algorithm integrating external correction of intermediate reasoning traces into Group Relative Policy Optimization (GRPO), leading to significant performance gains.
LLMs can now plan complex, long-horizon household tasks from ambiguous instructions with significantly improved performance, thanks to a novel reinforcement learning approach that corrects intermediate reasoning steps.
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.
