mlsanityData to 14 Oct 2026

Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification

arXiv 2506.00589 ↗First posted 31 May 2025cs.RO cs.LGcited by 0

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Abstract (arXiv)

Many core problems in robotics can be framed as constrained optimization problems. Often on these problems, the robotic system has uncertainty, or it would be advantageous to identify multiple high quality feasible solutions. To enable this, we present two novel frameworks for applying principles of constrained optimization to the new variational inference algorithm Stein variational gradient descent. Our general framework supports multiple types of constrained optimizers and can handle arbitrary constraints. We demonstrate on a variety of problems that we are able to learn to approximate distributions without violating constraints. Specifically, we show that we can build distributions of: robot motion plans that exactly avoid collisions, robot arm joint angles on the SE(3) manifold with exact table placement constraints, and object poses from point clouds with table placement constraints.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 44 references that are in the map. First posted on arXiv 31 May 2025.

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29 of 44 references.

References · 29

  1. 2016Variational Inference: A Review for Statisticianscited by 1,472
  2. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  3. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  4. 2020Projected Stein Variational Gradient Descentcited by 33
  5. 2020Stein Variational Model Predictive Controlcited by 30
  6. 2020f-Divergence Variational Inferencecited by 28
  7. 2019Feedback MPC for Torque-Controlled Legged Robotscited by 21
  8. 2018Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Networkcited by 19
  9. 2021Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulationcited by 17
  10. 2019Universal Boosting Variational Inferencecited by 13
  11. 2022Variational Inference MPC using Normalizing Flows and Out-of-Distribution Projectioncited by 13
  12. 2017Linear Matrix Inequalities for Physically-Consistent Inertial Parameter Identification: A Statistical Perspective on the Mass Distributioncited by 12
17 more
  1. 2021Variational Inference MPC using Tsallis Divergencecited by 11
  2. 2022Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descentcited by 11
  3. 2021Dual Online Stein Variational Inference for Control and Dynamicscited by 10
  4. 2023Constrained Stein Variational Trajectory Optimizationcited by 8
  5. 2021Stein ICP for Uncertainty Estimation in Point Cloud Matchingcited by 6
  6. 2022A Gaussian variational inference approach to motion planningcited by 6
  7. 2023Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehiclescited by 6
  8. 2020In-Hand Object-Dynamics Inference using Tactile Fingertipscited by 4
  9. 2022Stein Particle Filter for Nonlinear, Non-Gaussian State Estimationcited by 4
  10. 2023STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descentcited by 4
  11. 2024NLP Sampling: Combining MCMC and NLP Methods for Diverse Constrained Samplingcited by 4
  12. 2014A Novel Augmented Lagrangian Approach for Inequalities and Convergent Any-Time Non-Central Updatescited by 2
  13. 2015Invariant EKF Design for Scan Matching-aided Localizationcited by 2
  14. 2021Stein particle filteringcited by 2
  15. 2023Stochastic Motion Planning as Gaussian Variational Inference: Theory and Algorithmscited by 2
  16. 2023Stein Variational Belief Propagation for Multi-Robot Coordinationcited by 2
  17. 2024Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfacescited by 1

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