Robota-Embed: Unified VLA Embeddings for Robot In-Context Learning and Evolution
Retrieving useful context for a robot requires matching its task, execution stage, and the continuation that should follow. We introduce Robota-Embed, to our knowledge the first vision-language-action (VLA) embedding model to jointly encode all three modalities in both current-state queries and recorded future continuations. Predictive retrieval training aligns these endpoints, enabling historical continuations to serve as context at inference time. We construct a multi-source corpus of 1,695,684 cleaned training anchors and introduce Robota-Embed-Bench, with 4,000 queries and a shared 8,000-entry gallery. Robota-Embed achieves 47.33% clip-level and 81.77% episode-level R@1, exceeding act...
| Authors | Yunze Liu, Jing Wang, Shenrui Wu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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