Zimu Han

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.

Achieve both long-horizon planning and fine-grained control in robotic imitation learning by predicting action sequences at different frequencies.