X-Planner: Event-Structured Task Planning for Embodied Intelligence
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...
| Authors | Howard Lu, Shalfun Li, Porter Pan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 September 2026 |
| Last updated at source | 29 September 2026 |
Connected in the knowledge graph
- x-square-robot/xplanner-benchmarkDataset · cited by
Models and datasets referencing this paper
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.