Search papers, labs, and topics across Lattice.
2
70
4
1
Training a robot foundation model on 30,000 hours of heterogeneous embodied data lets it outperform prior methods by up to 48% on complex manipulation tasks and even benefit from low-quality data.
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.
