  Multi-fingered robotic grasping is an undeniable stepping stone to universal
picking and dexterous manipulation. Yet, multi-fingered grippers remain
challenging to control because of their rich nonsmooth contact dynamics or
because of sensor noise. In this work, we aim to plan hand configurations by
performing Bayesian posterior inference through the full stochastic forward
simulation of the robot in its environment, hence robustly accounting for many
of the uncertainties in the system. While previous methods either relied on
simplified surrogates of the likelihood function or attempted to learn to
directly predict maximum likelihood estimates, we bring a novel
simulation-based approach for full Bayesian inference based on a deep neural
network surrogate of the likelihood-to-evidence ratio. Hand configurations are
found by directly optimizing through the resulting amortized and differentiable
expression for the posterior. The geometry of the configuration space is
accounted for by proposing a Riemannian manifold optimization procedure through
the neural posterior. Simulation and physical benchmarks demonstrate the high
success rate of the procedure.
