An Intention-Aware Robust Safety Framework for Robot Teleoperation: Unifying Object Interaction and Obstacle Avoidance

Abstract

Control barrier functions (CBFs) have proven to be effective for obstacle avoidance in robot teleoperation systems. However, for classical CBF, model uncertainties and external disturbances can significantly degrade the robustness of safety control. Moreover, the fixed safety boundary lacks adaptability to dynamic switching on operational intentions. To address these limitations, this paper presents a hierarchical safety teleoperation framework that separates the safety layer from the leader-follower teleoperation layers. On this basis, a virtual proxy is introduced to construct a robust control-affine system decoupled from physical robot uncertainties and external disturbances. Building upon this, we propose an intention-aware adaptive control barrier function (IA-ACBF), which consists of two modules: intention detection and intention quantification. The intention detection module determines the operator's transient intention, which belongs to object interaction or obstacle avoidance. The intention quantification module then maps this to the adaptation of safety boundaries. Finally, the performance of the proposed method is validated through simulations and experiments with the physical robot.