  Motion planning framed as optimisation in structured latent spaces has
recently emerged as competitive with traditional methods in terms of planning
success while significantly outperforming them in terms of computational speed.
However, the real-world applicability of recent work in this domain remains
limited by the need to express obstacle information directly in state-space,
involving simple geometric primitives. In this work we address this challenge
by leveraging learned scene embeddings together with a generative model of the
robot manipulator to drive the optimisation process. In addition, we introduce
an approach for efficient collision checking which directly regularises the
optimisation undertaken for planning. Using simulated as well as real-world
experiments, we demonstrate that our approach, AMP-LS, is able to successfully
plan in novel, complex scenes while outperforming traditional planning
baselines in terms of computation speed by an order of magnitude. We show that
the resulting system is fast enough to enable closed-loop planning in
real-world dynamic scenes.
