SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918343717421056 |
|---|---|
| author | Zang, Zirui Amine, Ahmad Kokolakis, Nick-Marios T. Nghiem, Truong X. Rosolia, Ugo Mangharam, Rahul |
| author_facet | Zang, Zirui Amine, Ahmad Kokolakis, Nick-Marios T. Nghiem, Truong X. Rosolia, Ugo Mangharam, Rahul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16187 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks Zang, Zirui Amine, Ahmad Kokolakis, Nick-Marios T. Nghiem, Truong X. Rosolia, Ugo Mangharam, Rahul Robotics Artificial Intelligence Systems and Control I.2.9 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. |
| title | SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks |
| topic | Robotics Artificial Intelligence Systems and Control I.2.9 |
| url | https://arxiv.org/abs/2602.16187 |