mlsanityData to 14 Oct 2026

Stein Variational Model Predictive Control

arXiv 2011.07641 ↗First posted 15 Nov 2020cs.RO cs.AIcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 5; 2024: 6; 2025: 5; 2026: 2.

30 papers.

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  1. 2022Learning Implicit Priors for Motion Optimizationcited by 20
  2. 2021Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulationcited by 17
  3. 2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learningcited by 16
  4. 2024Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descentcited by 14
  5. 2022Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projectioncited by 13
  6. 2021Variational Inference MPC using Tsallis Divergencecited by 11
  7. 2021Dual Online Stein Variational Inference for Control and Dynamicscited by 10
  8. 2022Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processescited by 10
  9. 2022Learning Sampling Distributions for Model Predictive Controlcited by 10
  10. 2022Hierarchical Policy Blending as Inference for Reactive Robot Controlcited by 8
  11. 2023Constrained Stein Variational Trajectory Optimizationcited by 8
  12. 2023Deep Model Predictive Optimizationcited by 7
18 more
  1. 2023STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descentcited by 4
  2. 2024Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learningcited by 4
  3. 2021Efficient Stochastic Optimal Control through Approximate Bayesian Input Inferencecited by 3
  4. 2023Path Signatures for Diversity in Probabilistic Trajectory Optimisationcited by 3
  5. 2024Risk-sensitive control as inference with R'enyi divergencecited by 3
  6. 2023DiSProD: Differentiable Symbolic Propagation of Distributions for Planningcited by 2
  7. 2024Stein Variational Evolution Strategiescited by 2
  8. 2024Global Tensor Motion Planningcited by 2
  9. 2022Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradientcited by 1
  10. 2022Decision-Making Among Bounded Rational Agentscited by 1
  11. 2024Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fieldscited by 1
  12. 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 1
  13. 2025A Taxonomy of Self-Handovercited by 0
  14. 2025Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identificationcited by 0
  15. 2025Q-Guided Stein Variational Model Predictive Control via RL-informed Policy Priorcited by 0
  16. 2025Test-time reward-guided alignment of language models by importance sampling on pre-logit spacecited by 0
  17. 2026BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Samplingcited by 0
  18. 2026P3: Persistent Particle Planning for Constrained Diffusion Controlcited by 0