State-Wise Safe Learning and Control for Robotics
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 5
Special Issue Editor
Interests: robotics; safe control; reinforcement learning
Special Issue Information
Dear Colleagues,
Robotics is rapidly transitioning from controlled laboratory settings to complex, real-world applications. As these systems move beyond traditional boundaries, ensuring their safety and reliability becomes paramount. In recent years, safe learning and control has emerged as a vibrant research field that blends the adaptability of machine learning with the rigor of control theory to guarantee state-wise safety under real-world uncertainties. This Special Issue aims to bring together novel contributions from these intertwined domains, encompassing both theoretical advances and practical insights. We invite submissions that explore state-wise safe reinforcement learning, energy-function-based control, and data-driven modeling of dynamical systems. Submissions can include new algorithmic frameworks, breakthroughs in scalability, or innovative benchmarks for rigorous evaluation and assessment. Our goal is to showcase how theoretical rigor and hands-on implementation can converge to help autonomous robots operate with guaranteed safety. By highlighting cutting-edge methods and forging a path toward safe, reliable, and robust control, this Special Issue will serve as an essential forum for researchers and practitioners in robotics, artificial intelligence, and control systems.
Dr. Weiye Zhao
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- safe learning and control
- state-wise safety
- reinforcement learning
- dynamical systems modeling
- energy-function-based methods
- data-driven control
- robotics applications
- real-world autonomous systems
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