  We present a novel approach to path planning for robotic manipulators, in
which paths are produced via iterative optimisation in the latent space of a
generative model of robot poses. Constraints are incorporated through the use
of constraint satisfaction classifiers operating on the same space.
Optimisation leverages gradients through our learned models that provide a
simple way to combine goal reaching objectives with constraint satisfaction,
even in the presence of otherwise non-differentiable constraints. Our models
are trained in a task-agnostic manner on randomly sampled robot poses. In
baseline comparisons against a number of widely used planners, we achieve
commensurate performance in terms of task success, planning time and path
length, performing successful path planning with obstacle avoidance on a real
7-DoF robot arm.
