In robotics, designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers often do not consider the estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot's dynamics can lead to catastrophic behaviors. In this work, we present a risk-sensitive Extended Kalman Filter that allows doing output-feedback Model Predictive Control (MPC) safely. This filter adapts its estimation to the control objective. By taking a pessimistic estimate concerning the value function resulting from the MPC controller, the filter provides increased robustness to the controller in phases of uncertainty as compared to a standard Extended Kalman Filter (EKF). Moreover, the filter has the same complexity as an EKF, so that it can be used for real-time model-predictive control. The paper evaluates the risk-sensitive behavior of the proposed filter when used in a nonlinear model-predictive control loop on a planar drone and industrial manipulator in simulation, as well as on an external force estimation task on a real quadruped robot. These experiments demonstrate the abilities of the approach to improve performance in the face of uncertainties significantly.

翻译：在机器人学中，面对估计不确定性设计鲁棒算法是一项极具挑战性的任务。事实上，控制器通常不考虑估计不确定性，仅依赖最可能的估计状态。因此，环境或机器人动力的突变可能导致灾难性行为。本文提出了一种风险敏感扩展卡尔曼滤波器，能够安全实现输出反馈模型预测控制（MPC）。该滤波器可根据控制目标自适应调整估计。通过针对MPC控制器产生的价值函数采取悲观估计，该滤波器相比标准扩展卡尔曼滤波器（EKF），在不确定性阶段为控制器提供了更强的鲁棒性。此外，该滤波器与EKF具有相同的计算复杂度，可应用于实时模型预测控制。本文通过平面无人机与工业机械臂的仿真实验，以及真实四足机器人的外力估计任务，评估了所提滤波器在非线性模型预测控制回路中的风险敏感行为。这些实验证明，该方法能显著提升面对不确定性时的控制性能。