He Wang

Adding just 5% of target-embodiment data during pretraining can boost transfer performance by over 13 percentage points, highlighting the critical role of embodiment exposure in VLA models.

Reinforcement learning can significantly boost the efficiency of scheduling heterogeneous satellites, achieving better utility and convergence than traditional optimization methods.

IQACO not only boosts observation efficiency for agile satellites but also adapts dynamically to the unpredictable nature of maritime targets.

Forget painstakingly labeled real-world data – GraspVLA proves you can train a surprisingly capable grasping foundation model on a billion frames of purely synthetic action data.