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Stein Variational Model Predictive Control

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arxiv 2011.07641 v4 pith:6LY22LL2 submitted 2020-11-15 cs.RO cs.AI

Alexander Lambert , Adam Fishman , Dieter Fox , Byron Boots , Fabio Ramos This is my paper · ORCID
classification cs.ROcs.AI
keywords controlposteriorvariationaldecisiondistributionsmakingmethodsmodel
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Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitude of solutions as posterior distributions. By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem. We present a Stein variational gradient descent method to estimate the posterior directly over control parameters, given a cost function and observed state trajectories. We show that this framework leads to successful planning in challenging, non-convex optimal control problems.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation

    cs.RO 2026-05 unverdicted novelty 6.0 of 10

    SV-DRO evolves parameter particles via task-optimality-gap Stein gradients inside DRO-MPC, yielding up to 3× higher success on contact-rich manipulation under parametric uncertainty.

  2. Global Tensor Motion Planning

    cs.RO 2024-11 conditional novelty 6.0 of 10

    Global Tensor Motion Planning is a layered, tensor-based sampling planner with value iteration that plans many paths in batch on GPUs and claims probabilistic completeness.

  3. Transformer-Based Model Predictive Path Integral Control

    cs.RO 2024-12 conditional novelty 5.0 of 10

    TransformerMPPI uses a transformer trained on MPPI-generated trajectories to initialize the mean control sequence, reducing cost and sample counts in navigation and racing simulations.

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