用最优传输优化采样控制，提升非线性机器人任务成功率
Sampling-Based Control via Entropy-Regularized Optimal Transport
- 基于熵正则最优传输重构控制样本，避免模式平均问题
- 在导航/抓取/行走任务中成功率显著优于MPPI、CEM等方法
- 无需梯度计算，适合复杂动态系统的实时控制
基于采样的模型预测控制方法（如MPPI和CEM）对非线性机器人系统实现实时控制至关重要，尤其在存在不连续动力学而无法使用梯度优化的情况下。然而，这些方法源于信息论目标，忽略控制问题的几何结构，导致在复杂代价景观下出现模式平均等病态行为。本文提出OT-MPC，通过熵正则最优传输框架克服这些局限。该方法计算候选控制序列与低代价提议之间的最优耦合，将候选样本向邻近有希望的样本优化，同时协调整个样本集的更新以保持解空间覆盖。我们通过Sinkhorn算法推导出闭式、无梯度更新，实现实时性能。在导航、操作和运动任务上的实验表明，其成功率显著高于现有方法。
原文摘要 · Abstract (English)
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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