# X-Planner：面向具身智能的事件结构化任务规划

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-21 08:00
- AIHOT 分数：31
- AIHOT 链接：https://aihot.news/items/cmufi2yfb02wxro6ovjrzkja4
- 原文链接：https://arxiv.org/abs/2609.25187

## AI 摘要

X-Planner 是一个面向具身推理的规划前端，用事件结构化方案同时解决监督与表示问题。其规划数据整合 Ego、UMI 与遥操作数据并按来源区分标注深度，模型侧共享 VLM 主干提供离散事件状态与经 Staircase Decoding 传递连续 CoT 状态的隐式接口两种规划形式。

## 正文

Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems often leave this intermediate structure implicit. Existing chain-of-thought (CoT) planners also tend to rely on coarse task-level annotations or serialize long reasoning traces token by token. We present X-Planner, a planning front-end that addresses both the supervision and representation of embodied reasoning. Our planning data combine Ego, UMI, and teleoperation under a hierarchy granularity with source-dependent annotation depth.

Takeover-time annotations and human-designed failures supervise ongoing error recognition. On the model side, a shared VLM backbone exposes two event-structured plan forms: a discrete interface that emits interpretable event states and a latent interface that relays continuous CoT states across staggered Transformer depths through Staircase Decoding. A frozen latent-to-text reconstruction objective provides a semantic anchor for the latent representation. Offline two-step planning evaluation places X-Planner second among four evaluated models on both BERTScore-F1 and a judge-based Overall score.

In real-robot experiments, respectively, outperforming the evaluated baselines. These results characterize planning-text quality and downstream execution.