Uncertainty Aware-Predictive Control Barrier Functions : Safer human–robot interaction through probabilistic motion forecasting

Abstract

To enable flexible, high-throughput automation in settings where people and robots share workspaces, collaborative robotic cells must reconcile stringent safety guarantees with the need for responsive and effective behavior. A dynamic obstacle is the stochastic, task-dependent variability of human motion: when robots fall back on purely reactive or worst-case envelopes, they brake unnecessarily, stall task progress, and tamper with the fluidity that true Human–Robot Interaction (HRI) demands. In recent years, learning-based human-motion prediction has rapidly advanced, although most approaches produce worst-case scenario forecasts that often do not treat prediction uncertainty in a well-structured way, resulting in over-conservative planning algorithms, limiting their flexibility. This paper introduces Uncertainty-Aware Predictive Control Barrier Functions (UA-PCBFs), a unified framework that fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions (CBFs). In contrast to CBFs and other variants, our framework allows for a dynamic adjustment of the safety margin thanks to the human motion uncertainty estimation provided by the deep-learning forecasting module. Thanks to the awareness of prediction uncertainty, UA-PCBFs empower collaborative robots with a deeper understanding of future human states, facilitating more fluid and intelligent interactions through informed motion planning. Our key contribution is the first integration of epistemic prediction uncertainty directly into predictive CBFs, dynamically adjusting safety margins based on forecast confidence without assumptions about uncertainty evolution. We validate UA-PCBFs through comprehensive real-world experiments with an increasing level of realism, including automated setups (to perform exactly repeatable motions) with a robotic hand and direct human–robot interactions (to validate promptness, usability, and human confidence). Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics, significantly reducing the number of violations of the robot's safe space during interaction with respect to the state-of-the-art. Data and code will be released upon acceptance.