  Many Imitation and Reinforcement Learning approaches rely on the availability
of expert-generated demonstrations for learning policies or value functions
from data. Obtaining a reliable distribution of trajectories from motion
planners is non-trivial, since it must broadly cover the space of states likely
to be encountered during execution while also satisfying task-based
constraints. We propose a sampling strategy based on variational inference to
generate distributions of feasible, low-cost trajectories for high-dof motion
planning tasks. This includes a distributed, particle-based motion planning
algorithm which leverages a structured graphical representations for inference
over multi-modal posterior distributions. We also make explicit connections to
both approximate inference for trajectory optimization and entropy-regularized
reinforcement learning.
