标题： STEP规划器：构建跨层次子目标树作为具身的长时程任务规划器 显示英文标题

标题： STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner

摘要： 执行可靠长期任务规划的能力对于在现实环境中部署机器人至关重要。 然而，直接将大型语言模型（LLMs）作为动作序列生成器使用时，由于其在长期具身任务中的推理能力有限，通常会导致较低的成功率。 在STEP框架中，我们通过一对闭环模型构建一个子目标树：一个子目标分解模型和一个叶节点终止模型。 在此框架内，我们开发了一个从粗到细的分层树结构。 子目标分解模型利用基础LLM将复杂目标分解为可管理的子目标，从而扩展子目标树。 叶节点终止模型根据环境状态提供实时反馈，确定何时终止树的扩展，并确保每个叶节点可以直接转换为基本动作。 在VirtualHome WAH-NL基准和真实机器人上进行的实验表明，STEP在长期具身任务完成方面取得了高达34%（WAH-NL）和25%（真实机器人）的成功率，优于最先进方法。 显示英文摘要

摘要： The ability to perform reliable long-horizon task planning is crucial for deploying robots in real-world environments. However, directly employing Large Language Models (LLMs) as action sequence generators often results in low success rates due to their limited reasoning ability for long-horizon embodied tasks. In the STEP framework, we construct a subgoal tree through a pair of closed-loop models: a subgoal decomposition model and a leaf node termination model. Within this framework, we develop a hierarchical tree structure that spans from coarse to fine resolutions. The subgoal decomposition model leverages a foundation LLM to break down complex goals into manageable subgoals, thereby spanning the subgoal tree. The leaf node termination model provides real-time feedback based on environmental states, determining when to terminate the tree spanning and ensuring each leaf node can be directly converted into a primitive action. Experiments conducted in both the VirtualHome WAH-NL benchmark and on real robots demonstrate that STEP achieves long-horizon embodied task completion with success rates up to 34% (WAH-NL) and 25% (real robot) outperforming SOTA methods.