  Many previous works approach vision-based robotic grasping by training a
value network that evaluates grasp proposals. These approaches require an
optimization process at run-time to infer the best action from the value
network. As a result, the inference time grows exponentially as the dimension
of action space increases. We propose an alternative method, by directly
training a neural density model to approximate the conditional distribution of
successful grasp poses from the input images. We construct a neural network
that combines Gaussian mixture and normalizing flows, which is able to
represent multi-modal, complex probability distributions. We demonstrate on
both simulation and real robot that the proposed actor model achieves similar
performance compared to the value network using the Cross-Entropy Method (CEM)
for inference, on top-down grasping with a 4 dimensional action space. Our
actor model reduces the inference time by 3 times compared to the
state-of-the-art CEM method. We believe that actor models will play an
important role when scaling up these approaches to higher dimensional action
spaces.
