aiqbench analysis
This research introduces AHAT, a scalable framework combining LLMs with symbolic reasoning (PDDL) and reinforcement learning to execute long-horizon household tasks from ambiguous instructions. By integrating external correction into Group Relative Policy Optimization, the system effectively decomposes complex human intents while maintaining performance as environment size and constraint complexity increase. The work addresses critical scalability challenges in open-world robotic planning, moving beyond simple prompting toward robust symbolic-grounded execution.
Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
arXiv:2602.12244v2 Announce Type: replace Abstract: 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.
Abstract: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.
Robotics (cs.RO)
arXiv:2602.12244 [cs.RO]
(or arXiv:2602.12244v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2602.12244 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 274 words
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