  Synthesizing diverse and accurate grasps with multi-fingered hands is an
important yet challenging task in robotics. Previous efforts focusing on
generative modeling have fallen short of precisely capturing the multi-modal,
high-dimensional grasp distribution. To address this, we propose exploiting a
special kind of Deep Generative Model (DGM) based on Normalizing Flows (NFs),
an expressive model for learning complex probability distributions.
Specifically, we first observed an encouraging improvement in diversity by
directly applying a single conditional NFs (cNFs), dubbed FFHFlow-cnf, to learn
a grasp distribution conditioned on the incomplete point cloud. However, we
also recognized limited performance gains due to restricted expressivity in the
latent space. This motivated us to develop a novel flow-based d Deep Latent
Variable Model (DLVM), namely FFHFlow-lvm, which facilitates more reasonable
latent features, leading to both diverse and accurate grasp synthesis for
unseen objects. Unlike Variational Autoencoders (VAEs), the proposed DLVM
counteracts typical pitfalls such as mode collapse and mis-specified priors by
leveraging two cNFs for the prior and likelihood distributions, which are
usually restricted to being isotropic Gaussian. Comprehensive experiments in
simulation and real-robot scenarios demonstrate that our method generates more
accurate and diverse grasps than the VAE baselines. Additionally, a run-time
comparison is conducted to reveal its high potential for real-time
applications.
