论文标题
使用标准化流量和分发投影的变异推理MPC
Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projection
论文作者
论文摘要
我们提出了一种用于无冲突导航的模型预测控制方法（MPC）方法，该方法使用摊销的变分推断来通过训练在起始，目标和环境下进行归一化流程来近似最佳控制序列的分布。这种表示使我们能够学习一个分布，以说明机器人的动态和复杂的障碍物几何形状。然后，我们可以从该分布中取样，以产生控制序列，这些控制序列可能是目标定向的，并且是我们提出的基于FlowMppi采样的MPC方法的一部分。但是，在部署此方法时，机器人可能会遇到分布（OOD）环境，即与训练中使用的环境完全不同。在这种情况下，学习的流量不能被信任地产生低成本控制序列。为了将我们的方法推广到OOD环境，我们还提出了一种方法，该方法是对环境表示的投影，作为MPC过程的一部分。该投影将环境表示形式更改为更多的分配，同时还优化了真实环境中的轨迹质量。我们对2D双积分器和3D 12D的二聚体的仿真结果表明，在分布和OOD环境中，带有投影的FlowMppi胜过最先进的MPC基准，包括由现实世界数据产生的OOD环境。
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.
