mlsanityData to 14 Oct 2026

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations

arXiv 2507.04356 ↗First posted 6 Jul 2025math.OC cs.AI cs.ROcited by 0

Not on the map. Why?

Abstract (arXiv)

Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a policy for both lower level physical movement decisions as well as higher level conceptual tasks and their sub-components. In order to deliver greater safety and reliability in the system, we present the general formulation of this as a two-level optimization scheme which incorporates control at the lower level, and classical planning at the higher level, integrated with a capacity for learning. This synergistic integration of multiple methodologies -- control, classical planning, and RL -- presents an opportunity for greater insight for algorithm development, leading to more efficient and reliable performance. Here, the notion of reliability pertains to physical safety and interpretability into an otherwise black box operation of autonomous agents, concerning users and regulators. This work presents the necessary background and general formulation of the optimization framework, detailing each component and its integration with the others.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 84 references that are in the map. First posted on arXiv 6 Jul 2025.

Builds on

35 of 84 references.

References · 35

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  6. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  7. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
  8. 2017Learning Explanatory Rules from Noisy Datacited by 211
  9. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
  10. 2018Assessing Generalization in Deep Reinforcement Learningcited by 133
  11. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  12. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
23 more
  1. 2018A Single Time-Scale Stochastic Approximation Method for Nested Stochastic Optimizationcited by 53
  2. 2019Neural Logic Reinforcement Learningcited by 53
  3. 2019Control Regularization for Reduced Variance Reinforcement Learningcited by 37
  4. 2021A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learningcited by 24
  5. 2019Practical Reinforcement Learning of Stabilizing Economic MPCcited by 17
  6. 2022A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequencescited by 17
  7. 2020Learning for MPC with Stability & Safety Guaranteescited by 14
  8. 2018Online Planner Selection with Graph Neural Networks and Adaptive Schedulingcited by 13
  9. 2020Reinforcement Learning for Mixed-Integer Problems Based on MPCcited by 12
  10. 2021Reinforcement Learning based on Scenario-tree MPC for ASVscited by 8
  11. 2021MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storagecited by 8
  12. 2019Inter-Level Cooperation in Hierarchical Reinforcement Learningcited by 7
  13. 2021MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehiclescited by 7
  14. 2022Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systemscited by 7
  15. 2021Trilevel and Multilevel Optimization using Monotone Operator Theorycited by 6
  16. 2024Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systemscited by 6
  17. 2022A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivativescited by 5
  18. 2023Learning-based MPC from Big Data Using Reinforcement Learningcited by 5
  19. 2020Optimization with learning-informed differential equation constraints and its applicationscited by 4
  20. 2024Optimality Conditions for Model Predictive Control: Rethinking Predictive Model Designcited by 4
  21. 2017Dynamic Stochastic Approximation for Multi-stage Stochastic Optimizationcited by 3
  22. 2021Optimal Management of the Peak Power Penalty for Smart Grids Using MPC-based Reinforcement Learningcited by 3
  23. 2021Multi-agent Battery Storage Management using MPC-based Reinforcement Learningcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
  2. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
  3. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  4. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
  5. 2024A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot
  6. 2024Safe and Robust Reinforcement Learning: Principles and Practice
  7. 2025Skill-based Safe Reinforcement Learning with Risk Planning
  8. 2025Towards Safe Robot Foundation Models