Infinite-Horizon Value Function Approximation for Model Predictive Control

Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully desig...

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Veröffentlicht in:IEEE robotics and automation letters
Hauptverfasser: Jordana, Armand, Kleff, Sébastien, Haffemayer, Arthur, Ortiz-Haro, Joaquim, Carpentier, Justin, Mansard, Nicolas, Righetti, Ludovic
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 03.11.2025
Schlagworte:
Computer Science
Robotics
Robotics (cs.RO)
FOS: Computer and information sciences
ISSN:2377-3766
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Zusammenfassung:Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully design cost functions that can imitate an infinite horizon formulation, which is tedious and often results in local minima. In this work, we study how to approximate the infinite horizon value function of constrained optimal control problems with neural networks using value iteration and trajectory optimization. Furthermore, we demonstrate how using this value function approximation as a terminal cost provides global stability to the model predictive controller. The approach is validated on two toy problems and a real-world scenario with online obstacle avoidance on an industrial manipulator where the value function is conditioned to the goal and obstacle.
ISSN:2377-3766
DOI:10.48550/arXiv.2502.06760