Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction

2026-04-24

AI Summary

This work addresses the inherent conflict between safety and performance objectives in physical human–robot interaction by proposing a novel framework that integrates Control Barrier Functions (CBFs) with Hierarchical Quadratic Programming (HQP). The approach flexibly embeds safety and performance tasks across multiple HQP layers and incorporates a hierarchical relaxation mechanism to effectively resolve task conflicts, thereby significantly enhancing system feasibility and adaptability. Experimental validation on a real redundant robotic platform demonstrates that the proposed method simultaneously enforces real-time safety constraints and achieves high-performance interactive behaviors, highlighting its strong generality and practical utility.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting applications, e.g., collaborative robots for rehabilitation. Safety is critical for the successful deployment of this kind of robotic system. In recent years, Control Barrier Function (CBF) has emerged as an effective approach to enforce safety guarantees, which has been widely applied in various applications, from adaptive cruise control to navigation of legged robots. CBFs can be solved in a Quadratic Programming (QP) problem, which can include many CBF-formulated tasks. To manage a large number of safety tasks, a hierarchical CBF has been used to allow hierarchical relaxation of safety tasks to ensure the feasibility of a solution in the presence of conflicting tasks. In this work, we propose to use a CBF-based Hierarchical Quadratic Programming (HQP) framework in physical human-robot interaction to allow us to design both performance tasks (e.g., preserve the desired behavior at the human-robot interaction point) and safety tasks at any level of a hierarchy to balance the safety and the performance in a more flexible way. Extensive experiments were carried out on a real redundant robot to validate the effectiveness, flexibility, and generality of this approach.

Problem

• Human-Robot Interaction
• Safety
• Control Barrier Functions
• Hierarchical Quadratic Programming
• Task Conflicts

Innovation

• Control Barrier Function
• Hierarchical Quadratic Programming
• Safe Human-Robot Interaction
• Task Prioritization
• Redundant Robot