Shared autonomy blends operator intent with autonomous assistance. In cluttered environments, linear blending can produce unsafe commands even when each source is individually collision-free. Many existing approaches model obstacle avoidance through potentials or cost terms, which only enforce safety as a soft constraint. In contrast, safety-critical control requires hard guarantees. We investigate the use of control barrier functions (CBFs) at the inverse kinematics (IK) layer of shared autonomy, targeting post-blend safety while preserving task performance. Our approach is evaluated in simulation on representative cluttered environments and in a VR teleoperation study comparing pure teleoperation with shared autonomy. Across conditions, employing CBFs at the IK layer reduces violation time and increases minimum clearance while maintaining task performance. In the user study, participants reported higher perceived safety and trust, lower interference, and an overall preference for shared autonomy with our safety filter. Additional materials available at https://berkguler.github.io/barrierik.

翻译：共享自主将操作者意图与自主辅助相结合。在杂乱环境中，线性混合可能产生不安全指令，即使各输入源单独考虑时均无碰撞风险。现有方法多通过势场或代价项建模避障，仅将安全性作为软约束。相比之下，安全攸关控制需要严格的安全保证。本研究探索在共享自主的逆运动学层应用控制屏障函数，旨在保障混合后指令安全性的同时维持任务性能。我们在典型杂乱环境仿真及VR遥操作实验中评估了该方法，对比了纯遥操作与共享自主模式。所有实验条件下，在逆运动学层应用控制屏障函数均能减少约束违反时间、增大最小安全距离，同时保持任务性能。用户研究中，参与者报告了更高的感知安全性与信任度、更低的操作干涉感，并总体倾向于采用本安全滤波器的共享自主模式。补充材料详见 https://berkguler.github.io/barrierik。