Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large language models (LLMs), specifically GPT-2, in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method grounds the input of the LLM on the domain that is represented as a scene graph, enabling it to translate human requests into executable robot plans, thereby learning to reason over long-horizon tasks, as encountered in the ALFRED benchmark. We compare our approach with classical planning and baseline methods to examine the applicability and generalizability of LLM-based planners. Our findings suggest that the knowledge stored in an LLM can be effectively grounded to perform long-horizon task planning, demonstrating the promising potential for the future application of neuro-symbolic planning methods in robotics.

翻译：长时域任务规划对于智能辅助及服务机器人的发展至关重要。本文探究了较小规模的大语言模型（LLMs），特别是GPT-2，在机器人任务规划中的适用性，通过让模型学习将任务分解为子目标规范，供规划器顺序执行。我们将LLM的输入接地于以场景图表示的领域上，使其能够将人类请求转化为可执行的机器人规划，从而学习对长时域任务进行推理——如ALFRED基准测试中的任务。我们将本方法与经典规划及基线方法进行比较，以考察基于LLM的规划器的适用性与泛化能力。研究结果表明，LLM中存储的知识可被有效接地以执行长时域任务规划，展现了神经符号规划方法在机器人领域未来应用的巨大潜力。