Enshen Zhou

MA-VLA achieves robust multi-arm compositional generalization, outperforming existing models by effectively enabling role-agnostic behavior in unseen collaboration scenarios.

By decoupling camera and manipulation actions and training them in a coordinated manner, SaPaVe achieves significantly higher success rates in real-world robotic manipulation tasks compared to existing end-to-end vision-language-action models.