Paper published in IEEE Robotics and Automation Letters (RA-L)

A paper titled “SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks” has been published in the journal IEEE Robotics and Automation Letters. The paper develops an iterative control framework based on an information-theoretic model predictive control algorithm to address a constrained infinite-horizon optimal control problem for discrete-time nonlinear stochastic systems. Autonomous race car simulations and real-world experiments were conducted to validate and demonstrate the effectiveness of the framework; see our project website. This paper is a collaboration with Prof. Rahul Mangharam’s x-lab at the University of Pennsylvania.