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arXiv · 2605.02147

Sampling-Based Control via Entropy-Regularized Optimal Transport

Vincent Pacelli·Akash Ratheesh·Evangelos A. Theodorou
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Abstract

Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where discontinuous dynamics preclude gradient-based optimization. However, these methods derive from information-theoretic objectives that are agnostic to the geometry of the control problem, leading to pathological behaviors such as mode-averaging when the cost landscape is complex. We present OT-MPC, a sampling-based algorithm that overcomes these limitations through an entropy-regularized optimal transport formulation. By computing an optimal coupling between candidate control sequences and low-cost proposals, OT-MPC refines candidates toward nearby promising samples while coordinating updates across the ensemble to maintain coverage of the solution space. We derive closed-form, gradient-free updates via the Sinkhorn algorithm, enabling real-time performance. Experiments on navigation, manipulation, and locomotion tasks demonstrate improved success rates over existing methods.

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Vincent Pacelli, Akash Ratheesh, Evangelos A. Theodorou. 2026-05-04. Sampling-Based Control via Entropy-Regularized Optimal Transport. https://arxiv.org/abs/2605.02147

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