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

arXiv: https://arxiv.org/abs/2602.16187 · HTML: https://mlsanity.com/paper/2602.16187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework](https://mlsanity.com/paper/1609.01387/) (2016)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions](https://mlsanity.com/paper/2003.01410/) (2020)
- [Sample-Based Learning Model Predictive Control for Linear Uncertain Systems](https://mlsanity.com/paper/1904.06432/) (2019)
- [Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions](https://mlsanity.com/paper/2302.11719/) (2023)
- [Performance-driven Constrained Optimal Auto-Tuner for MPC](https://mlsanity.com/paper/2503.07127/) (2025)
- [Computing Robust Controlled Invariant Sets of Linear Systems](https://mlsanity.com/paper/1601.00416/) (2016)
- [On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller](https://mlsanity.com/paper/2010.15153/) (2020)
- [Time-Optimal Navigation in Uncertain Environments with High-Level Specifications](https://mlsanity.com/paper/2103.01476/) (2021)
- [PRIEST: Projection Guided Sampling-Based Optimization For Autonomous Navigation](https://mlsanity.com/paper/2309.08235/) (2023)
- [Robust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks](https://mlsanity.com/paper/1911.09234/) (2019)

## Abstract (arXiv)

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.