Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance
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Abstract (arXiv)
We tackle the data-driven chance-constrained density steering problem using the Gromov-Wasserstein metric. The underlying dynamical system is an unknown linear controlled recursion, with the assumption that sufficiently rich input-output data from pre-operational experiments are available. The initial state is modeled as a Gaussian mixture, while the terminal state is required to match a specified Gaussian distribution. We reformulate the resulting optimal control problem as a difference-of-convex program and show that it can be efficiently and tractably solved using the DC algorithm. Numerical results validate our approach through various data-driven schemes.
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References · 11
- 2019Formulas for Data-driven Control: Stabilization, Optimality and Robustnesscited by 47
- 2021Belief Space Planning: A Covariance Steering Approachcited by 5
- 2024Minimum energy density steering of linear systems with Gromov-Wasserstein terminal costcited by 3
- 2023Computationally Efficient Covariance Steering for Systems Subject to Parametric Disturbances and Chance Constraintscited by 2
- 2023Density Steering of Gaussian Mixture Models for Discrete-Time Linear Systemscited by 2
- 2024Chance-Constrained Gaussian Mixture Steering to a Terminal Gaussian Distributioncited by 2
- 2025Formation Shape Control using the Gromov-Wasserstein Metriccited by 2
- 2023Covariance Steering for Systems Subject to Unknown Parameterscited by 1
- 2023Data-Driven Covariance Steering Control Designcited by 1
- 2023Data-Driven Robust Covariance Control for Uncertain Linear Systemscited by 1
- 2024Stochastic Control of UAVs: An Optimal Tradeoff between Performance, Flight Smoothness and Control Effortcited by 1
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