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Generative models are a promising avenue for learning generalizable robotic tasks from data. A
fundamental task that remains a challenge to autonomous manipulation is the 6-DoF grasping of
unknown objects. This work proposes Grasp-O a simple, fast, and robust system for general-purpose
vision-based 6-DoF grasping applications. Our system is built using a powerful and efficient
Variational Autoencoder (VAE) that learns a distribution of SE(3) grasp poses conditioned on
object point clouds. The generative model is complemented by a grasp classification network that
discriminates between good and bad grasp. We conduct extensive evaluations in simulation and the
real world and demonstrate that our system outperforms existing VAE-based ones.
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