Entropy Regularized Motion Planning via Stein Variational Inference

2021/07/11 by Lambert, Alexander, Boots, Byron · 1 citation
#FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2107.05146

Abstract

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.

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