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HIL-UMI is introduced, a policy-guided Universal Manipulation Interface (UMI) framework for robot-free human-in-the-loop VLA post-training that preserves the iterative and policy-aware nature of human-in-the-loop learning while decoupling data collection from robot deployment.
This work introduces a comprehensive evaluation framework organized around four complementary dimensions of physical world reasoning, and constructs a diverse set of novel tasks that require models to integrate complementary information across modalities.