MonLog: MONotonic-Constrained LOGistic Regressions for Automated Safety Curve Design

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.