用分层子目标树提升机器人长程任务规划成功率
STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner
- 构建双向闭环模型生成分层子目标树，从粗到细分解任务
- 在虚拟环境和真实机器人上分别达到34%和25%的成功率
- 适合需要复杂长程规划的具身智能系统研究者
在真实环境中部署机器人离不开可靠的长程任务规划能力。然而，直接使用大语言模型（LLMs）生成动作序列时，由于对长程具身任务推理能力有限，常导致成功率低。本文提出的STEP框架通过一对闭环模型构建子目标树：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，扩展子目标树；叶节点终止模型基于环境状态提供实时反馈，判断何时终止树的扩展，确保每个叶节点可直接转为基本动作。在VirtualHome WAH-NL基准和真实机器人上的实验表明，STEP实现了最高34%（WAH-NL）和25%（真实机器人）的长程任务完成率，优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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