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

arXiv: https://arxiv.org/abs/2506.21030 · HTML: https://mlsanity.com/paper/2506.21030/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2025.

## Built on by (most cited first, 7 of 7)

- [Human Centric General Physical Intelligence for Agile Manufacturing Automation](https://mlsanity.com/paper/2508.11960/) (2025)
- [Embodied Tree of Thoughts: Deliberate Manipulation Planning with Embodied World Model](https://mlsanity.com/paper/2512.08188/) (2025)
- [Towards a General Framework for HTN Modeling with LLMs](https://mlsanity.com/paper/2511.18165/) (2025)
- [STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Order](https://mlsanity.com/paper/2601.08107/) (2026)
- [Testing and Evaluation of Agentic AI Systems In Military Command and Control](https://mlsanity.com/paper/2608.20597/) (2026)
- [Meta-Ctrl: Guaranteed Plan Generation by Decoupling Syntactic and Semantic Constraints](https://mlsanity.com/paper/2608.22149/) (2026)
- [Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation](https://mlsanity.com/paper/2608.22800/) (2026)

## Abstract (arXiv)

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.