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

2026-02-18

AI Summary

This work proposes an information-theoretic iterative learning model predictive control framework to address the integrated demands of safety, robustness, and high performance in robotic iterative tasks operating under complex and uncertain environments. The approach leverages historical trajectories to learn a value function, employs normalizing flows to model non-Gaussian uncertainties, and incorporates an adaptive safety penalty mechanism to handle infinite-horizon constrained optimization for nonlinear stochastic systems. Efficient real-time solutions are achieved through highly parallelized GPU implementation. Both simulation and hardware experiments demonstrate that the system consistently improves control performance across iterations while rigorously satisfying safety constraints, thereby validating the effectiveness and superiority of the proposed method.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Natural Language Processing: Safety and Robustness
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• iterative tasks
• safety
• nonlinear stochastic systems
• optimal control
• uncertainty

Innovation

• information-theoretic control
• learning model predictive control
• normalizing flows
• adaptive penalty method
• GPU parallelization