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Many Imitation and Reinforcement Learning approaches rely on the availabilityof expert-generated demonstrations for learning policies or value functionsfrom data. Obtaining a reliable distribution of trajectories from motionplanners is non-trivial, since it must broadly cover the space of states likelyto be encountered during execution while also satisfying task-basedconstraints. We propose a sampling strategy based on variational inference togenerate distributions of feasible, low-cost trajectories for high-dof motionplanning tasks. This includes a distributed, particle-based motion planningalgorithm which leverages a structured graphical representations for inferenceover multi-modal posterior distributions. We also make explicit connections toboth approximate inference for trajectory optimization and entropy-regularizedreinforcement learning.

Entropy Regularized Motion Planning via Stein Variational Inference