X-Planner: Event-Structured Task Planning for Embodied Intelligence
X-Planner adds event-structured discrete and latent plans for long-horizon vision-language-action robot tasks.
X-Planner is a planning front-end for long-horizon embodied manipulation that makes intermediate task structure explicit for vision-language-action systems. Its data combine Ego, UMI, and teleoperation with source-dependent annotation depth, takeover-time labels, and human-designed failures. A shared VLM exposes discrete event states and continuous chain-of-thought states through Staircase Decoding, anchored by frozen latent-to-text reconstruction. Offline, it placed second of four models on BERTScore-F1 and a judge Overall score, and real-robot runs beat the evaluated baselines.
- Mixes Ego, UMI, and teleoperation data with hierarchy-aware annotations.
- Shared VLM emits discrete events or latent states via Staircase Decoding.
- Ranked second of four models on BERTScore-F1 and judge Overall.
- Real-robot experiments outperformed the evaluated baselines.
From the original article
1 min read at huggingface.co
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.
By Howard Lu, Shalfun Li, Porter Pan · huggingface.co
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