Problem
Research questions and friction points this paper is trying to address.
Task Planning
Vision-Language-Action Systems
Chain-of-Thought Planners
Embodied Reasoning
Supervision
X-Planner: Event-Structured Task Planning for Embodied Intelligence
🤖 AI Summary
X-Planner通过结合多种数据源和创新的事件结构规划形式，解决了现代VLA系统中任务规划中间结构隐含的问题，提高了机器人任务执行的质量。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Event-Structured Task Planning
Staircase Decoding
Shared VLM Backbone
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