Ji-Yao Zhang

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 studies open-vocabulary task-oriented dexterous grasp generation, where a robot must infer functional intent from free-form language, ground it in multi-view visual observations and object geometry, and generate an executable high-degree-of-freedom grasp.

Scaling from 120,000 hours of egocentric video boosts robotic manipulation success rates from 36.1% to 77.8% in zero-shot evaluations.

A novel data synthesis approach enables a lightweight model to outperform state-of-the-art planning systems by over 10x in household appliance manipulation tasks.

Bridging the gap between human manipulation and robotic control, JoyAI-RA unlocks enhanced cross-embodiment behavior learning through multi-source pretraining.

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