Learning a Generalizable Trajectory Sampling Distribution for Model Predictive Control
Thomas Power, Dmitry Berenson
Abstract
We propose a sample-based Model Predictive Control (MPC) method for collision-free navigation that uses a normalizing flow as a sampling distribution, conditioned on the start, goal, environment and cost parameters. This representation allows us to learn a distribution that accounts for both the dynamics of the robot and complex obstacle geometries. We propose a way to incorporate this sampling distribution into two sampling-based MPC methods, MPPI and iCEM. However, when deploying these methods, the robot may encounter an out-of-distribution (OOD) environment. To generalize our method to OOD environments we also present an approach that performs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">projection</i> on the representation of the environment. This projection changes the environment representation to be more in-distribution while also optimizing trajectory quality in the true environment. Our simulation results on a 2D double-integrator, a 12DoF quadrotor and a 7DoF kinematic manipulator suggest that using a learned sampling distribution with projection outperforms MPC baselines on both in-distribution and OOD environments over different cost functions, including OOD environments generated from real-world data.