Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection. (arXiv:2205.04667v1 [cs.RO])

Web: http://arxiv.org/abs/2205.04667

We propose a Model Predictive Control (MPC) method for collision-free
navigation that uses amortized variational inference to approximate the
distribution of optimal control sequences by training a normalizing flow
conditioned on the start, goal and environment. This representation allows us
to learn a distribution that accounts for both the dynamics of the robot and
complex obstacle geometries. We can then sample from this distribution to
produce control sequences which are likely to be both goal-directed and
collision-free as part of …

arxiv distribution inference projection