论文标题
大鼠ILQR：风险自动调整控制器，可最佳地解释随机模型不匹配
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
论文作者
论文摘要
随机环境中成功的机器人操作取决于对潜在概率分布的准确表征，但是由于知识有限，这通常是不完美的。这项工作提出了一种能够处理此类分布不匹配的控制算法。具体而言，我们提出了一种新型的非线性MPC，用于分布稳健的控制，该MPC计划在当地最佳的反馈策略，而不是在给定的KL差异中与高斯分布绑定的最差分布分布。我们的框架利用分配稳健控制与风险敏感的最佳控制之间的数学等效性，还提供了一种算法，以动态调整在线风险敏感性水平以进行风险敏感控制。在动态碰撞的避免方案中，证明了分布鲁棒性以及自动风险敏感性调整的好处，其中人类运动的预测分布是错误的。
Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally robust control, which plans locally optimal feedback policies against a worst-case distribution within a given KL divergence bound from a Gaussian distribution. Leveraging mathematical equivalence between distributionally robust control and risk-sensitive optimal control, our framework also provides an algorithm to dynamically adjust the risk-sensitivity level online for risk-sensitive control. The benefits of the distributional robustness as well as the automatic risk-sensitivity adjustment are demonstrated in a dynamic collision avoidance scenario where the predictive distribution of human motion is erroneous.
