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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.04356 |
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| _version_ | 1866918425892225024 |
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| author | Kungurtsev, Vyacheslav Naibei, Monicah Cherop Sir, Gustav Anand, Akhil Gros, Sebastien Tian, Haozhe Hamedmoghadam, Homayoun |
| author_facet | Kungurtsev, Vyacheslav Naibei, Monicah Cherop Sir, Gustav Anand, Akhil Gros, Sebastien Tian, Haozhe Hamedmoghadam, Homayoun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04356 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations Kungurtsev, Vyacheslav Naibei, Monicah Cherop Sir, Gustav Anand, Akhil Gros, Sebastien Tian, Haozhe Hamedmoghadam, Homayoun Optimization and Control Artificial Intelligence Robotics 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. |
| title | Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations |
| topic | Optimization and Control Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2507.04356 |