SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Zirui Zang, Ahmad Amine, Nick-Marios Kokolakis, Truong Xuan Nghiem, Ugo Rosolia, Rahul Mangharam
AI summary
Problem
Iterative robotic tasks in uncertain environments require balancing safety, robustness, and performance, but existing constrained model predictive control methods are often overly conservative, lack general constraint handling, or cannot run in real-time.
Approach
The authors introduce SIT-LMPC, which combines information-theoretic MPC with an online adaptive penalty method to enforce constraints dynamically, while using normalizing flows to learn complex cost distributions from past trajectories, all optimized for GPU parallelization.
Key results
- Online adaptive penalty method dynamically balances safety and optimality
- Normalizing flows model iteration costs with richer uncertainty than Gaussian priors
- Achieves 100Hz+ real-time control on hardware via full GPU parallelization
- Demonstrates iterative performance gains and robust constraint satisfaction in simulations and experiments
Why it matters
Provides a scalable, real-time control framework for safe iterative robotics, advancing applications in autonomous navigation, aerial systems, and precision automation.
Abstract
Robots executing iterative tasks in complex, un- certain environments require control strategies that balance robustness, safety, and high performance. This paper 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 nonlin- ear 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.