MonLog: MONotonic-Constrained LOGistic Regressions for Automated Safety Curve Design
Melone, Alessandro Kirschner, Robin Jeanne Müller, Dirk Swikir, Abdalla Haddadin, Sami
Melone, Alessandro
Kirschner, Robin Jeanne
Müller, Dirk
Swikir, Abdalla
Haddadin, Sami
Supervisor
Department
Robotics
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
The increasing integration of robots in close human environments necessitates robust safety measures that can adapt to evolving tasks and conditions. Current standards rely on task-specific safety evaluations that are often inflexible, requiring repeated assessments whenever task parameters change. This work proposes MonLog, a data-driven, probabilistic method to automatically derive safety curves (SCs) from recent injury protection data sets. By leveraging non-linear modeling techniques, our approach addresses the limitations of conventional linear SCs, which often result in overly conservative speed restrictions. We present a comprehensive test routine to validate our method, highlighting improvements in both compliance with safety constraints and operational efficiency. Our findings demonstrate that the proposed approach not only enhances safety but also optimizes robotic performance, making it suitable for a wide range of applications.
Citation
A. Melone, R. J. Kirschner, D. Müller, A. Swikir and S. Haddadin, "MonLog: MONotonic-Constrained LOGistic Regressions for Automated Safety Curve Design," 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 14315-14322, doi: 10.1109/ICRA55743.2025.11128207.
Source
International Conference on Robotics and Automation (ICRA)
Conference
2025 IEEE International Conference on Robotics and Automation (ICRA)
Keywords
Logistic Regression, Adaptation Models, Current Measurement, Probabilistic Logic, Polynomials, Protection, Injuries, Robots, Standards
Subjects
Source
2025 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE