Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection

TL;DR

This paper introduces FlowMPPI, a variational inference-based MPC method using normalizing flows for collision-free navigation, enhanced with environment projection to handle out-of-distribution scenarios, demonstrating superior performance in simulations.

Contribution

The paper presents a novel MPC approach combining normalizing flows with environment projection to improve collision avoidance and OOD generalization in robotic navigation.

Findings

FlowMPPI outperforms state-of-the-art MPC baselines in simulations.

Environment projection improves OOD environment handling.

Method is effective on both 2D and 3D robotic systems.

Abstract

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 our proposed FlowMPPI sampling-based MPC method. However, when deploying this method, the robot may encounter an out-of-distribution (OOD) environment, i.e. one which is radically different from those used in training. In such cases, the learned flow cannot be trusted to produce low-cost control sequences. To generalize our method to OOD environments we also present an approach that performs projection on the representation of the environment as part of the MPC process. 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 and a 3D 12DoF underactuated quadrotor suggest that FlowMPPI with projection outperforms state-of-the-art MPC baselines on both in-distribution and OOD environments, including OOD environments generated from real-world data.