SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation
Robotic deployment in real-world environments depends on rich, domain-specific action data as much as on strong model architecture. General-purpose robot foundation models show modest performance in complex unseen tasks such as manipulation in a retail domain when applied out of the box. The root cause is a data gap: retail environments are structurally absent from general robot pretraining distributions, and the path to filling that gap through teleoperation is prohibitively expensive, logistically constrained, and difficult to scale. We introduce SABER, a high-fidelity retail robotics action dataset built from over 100 hours of natural in-store capture across multiple real grocery envir...
| Authors | Narsimha Menga, Parikshit Sakurikar, Amirreza Rouhi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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