  In this work we present CppFlow - a novel and performant planner for the
Cartesian Path Planning problem, which finds valid trajectories up to 129x
faster than current methods, while also succeeding on more difficult problems
where others fail. At the core of the proposed algorithm is the use of a
learned, generative Inverse Kinematics solver, which is able to efficiently
produce promising entire candidate solution trajectories on the GPU. Precise,
valid solutions are then found through classical approaches such as
differentiable programming, global search, and optimization. In combining
approaches from these two paradigms we get the best of both worlds - efficient
approximate solutions from generative AI which are made exact using the
guarantees of traditional planning and optimization. We evaluate our system
against other state of the art methods on a set of established baselines as
well as new ones introduced in this work and find that our method significantly
outperforms others in terms of the time to find a valid solution and planning
success rate, and performs comparably in terms of trajectory length over time.
The work is made open source and available for use upon acceptance.
