SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks

• URL: http://arxiv.org/abs/2602.16187v1
• Date: Wed, 18 Feb 2026 05:13:45 GMT
• Title: SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
• Authors: Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis, Truong X. Nghiem, Ugo Rosolia, Rahul Mangharam,
• Abstract summary: We introduce a safe information-theoretic learning model predictive control algorithm for iterative tasks.<n>An adaptive penalty method is developed to ensure safety while balancing optimality.<n>We show that SIT-LMPC iteratively improves system performance while robustly satisfying system constraints.
• Score: 2.661015608942385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots executing iterative tasks in complex, uncertain 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 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.

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