Safe Robot Reflexes: A Taxonomy-Based Decision and Modulation Framework

Recent advances in control and planning allow for seamless physical human–robot interaction (pHRI). At the same time, novel challenges appear in orchestrating intelligent decision-making and ensuring safe control of robots. Particularly in scenarios involving unforeseen or unintended collisions, robots face the imperative of reacting judiciously to avert potential risks to humans, other robots, obstacles, or themselves. At the same time, they need to maintain focus on their primary task or be able to safely resume it. Collision detection and identification algorithms are now well established in industry, yet complex collision reflexes have not transitioned into industrial applications beyond basic stopping reactions. Despite the introduction of numerous advanced high-performance reflex controllers over the past decades, their real-world adoption has remained a challenge. This work establishes a systematic framework to address that gap. For this, the reflex control problem is defined, reflex behaviors are systematically classified and categorized, and relevant safety data is acquired following existing international standards. We argue that this foundational step is crucial for improving the safety and capabilities of robots in both complex industrial and domestic environments. We validate our approach within the system class of articulated manipulators through a state-of-the-art cooperative pick-and-place task, providing a blueprint for future implementations for other robot classes.