STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911058582568960 |
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| author | Zhou, Tianxing Wang, Zhirui Ao, Haojia Chen, Guangyan Xing, Boyang Cheng, Jingwen Yang, Yi Yue, Yufeng |
| author_facet | Zhou, Tianxing Wang, Zhirui Ao, Haojia Chen, Guangyan Xing, Boyang Cheng, Jingwen Yang, Yi Yue, Yufeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21030 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner Zhou, Tianxing Wang, Zhirui Ao, Haojia Chen, Guangyan Xing, Boyang Cheng, Jingwen Yang, Yi Yue, Yufeng Robotics 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. |
| title | STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.21030 |