Peking University and Qiyuan Robotics Introduce HIL-UMI for Robot-Free Post-Training Data Collection

Dong Hao’s team at Peking University and Qiyuan Robotics introduced HIL-UMI, a research system that collects targeted corrections for robot post-training through handheld UMI demonstrations without a robot present, and reported improved task progress and faster effective data collection in testing. HIL-UMI detects differences between human actions and model predictions, then uses an advantage estimator to prioritize actions that advance a task. Across four physical robot tasks, the team reported average task progress of 94.8, compared with 58.0 for continued supervised fine-tuning (SFT). On table organization, effective data collection was reported to be 5.63 times faster than HG-DAgger. The robot-free capability applies to collecting post-training demonstrations; the task-performance results came from physical robot tests.

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