Context-aware Cost Shaping to Reduce the Impact of Model Error in Receding Horizon Control

Abstract

This paper presents a method to enable a robot using stochastic Model Predictive Control (MPC) to achieve high performance on a repetitive path-following task. In particular, we consider the case where the accuracy of the model for robot dynamics varies significantly over the path-motivated by the fact that the models used in MPC must be computationally efficient, which limits their expressive power. Our approach is based on correcting the cost predicted using a simple learned dynamics model over the MPC horizon. This discourages the controller from taking actions that lead to higher cost than would have been predicted using the dynamics model. In addition, stochastic MPC provides a quantitative measure of safety by limiting the probability of violating state and input constraints over the prediction horizon. Our approach is unique in that it combines both online model learning and cost learning over the prediction horizon and is geared towards operating a robot in changing conditions. We demonstrate our algorithm in simulation and experiment on a ground robot that uses a stereo camera for localization.