Mi Yan

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.

KPGrasp achieves a remarkable 76.3% grasp success rate, outpacing existing methods by a staggering 47.4% while simplifying the grasp generation process.

Forget hand-crafted heuristics: this new dynamics-aware policy learns to exploit contact forces in cluttered environments, outperforming traditional methods by 25% in simulation and showing impressive sim-to-real transfer.

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.