Foundation Models

Robot Utility Models (RUM)

Robot Utility Models (RUMs) are pretrained task-specific manipulation policies, introduced by NYU and Hello Robot researchers in 2024, designed to deploy zero-shot in previously unseen environments without any fine-tuning. Each model is trained on roughly a thousand demonstrations gathered across dozens of real environments with the Stick, an inexpensive smartphone-based handheld data collection tool, and deployment adds a vision-language-model success verifier that triggers retries, reaching roughly 90 percent success on tasks such as opening doors and drawers.

Why it matters for physical AI

RUMs put concrete numbers on the data diversity needed for zero-shot generalization and showed that cheap handheld collection plus self-verification can yield deployable skills without on-site training.