SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data. As an alternative, we study one-shot demo-conditioned VLAs, where a robot policy is conditioned on a single demonstration video of an unseen task. We find that existing end-to-end approaches often struggle when successful execution requires precisely localizing small target regions. To address this limitation, we propose SeeTraceAct, a demo-conditioned VLA framework that encourages precise spatial grounding through visibility-aware prediction of future end-effector traces. To enable reproducible evaluation with cross-...
| Authors | Jaehyeon Son, Junhyun Kim, Kyle Kam et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 June 2026 |
| Last updated at source | 13 October 2026 |
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