Pelican-VLA 0.5: Attending Before Acting Benefits Generalization
In this report, we present Pelican-VLA 0.5, a unified VLA model that integrates vision-language understanding, future-frame generation, and action prediction within a single architecture. Pelican-VLA 0.5 achieves attention-level generalization: without object annotations, segmentation masks, attention supervision, or task-specific fine-tuning, its action pathway already focuses on the manipulation-relevant object and contact region. This behavior persists across unseen scenes and unseen robot embodiments, and is substantially stronger than in other open-source VLA baselines. We verify that this ability originates from the learnable Bottleneck Token inserted between perception and action: ...
| Authors | Zeyuan Ding, Wenhai Liu, Yang Xu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 July 2026 |
| Last updated at source | 9 July 2026 |
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