Risk-Sensitive Extended Kalman Filter

TL;DR

This paper introduces a risk-sensitive Extended Kalman Filter that enhances robustness in model predictive control for robotics by accounting for estimation uncertainty, demonstrated through simulations and real robot experiments.

Contribution

It presents a novel risk-sensitive EKF that adapts to control objectives, improving robustness without increasing computational complexity.

Findings

Enhanced robustness to uncertainty in control tasks

Improved performance in simulation and real robot experiments

Maintains real-time computational complexity

Abstract

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.