Abstract

Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This letter introduces a safe information-theoretic learning model predictive control (SIT-LMPC) algorithm for iterative tasks. Specifically, we design 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. An adaptive penalty method is developed to ensure safety while balancing optimality. Trajectories from previous iterations are utilized to learn a value function using normalizing flows, which enables richer uncertainty modeling compared to Gaussian priors. SIT-LMPC is designed for highly parallel execution on graphics processing units, allowing efficient real-time optimization. Benchmark simulations and hardware experiments demonstrate that SIT-LMPC iteratively improves system performance while robustly satisfying system constraints.

Simulated Experiment

Recorded video of the experiments on map 1: Left: live simulation of the vehicle, displaying planned trajectory in MPPI horizon (yellow), and the control invariant safe set (pink). Right: Speed profile showing the evolution of trajectories.

 Real-world Experiment