A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions

TL;DR

This paper introduces a safety-aware shared autonomy framework using control barrier functions at the inverse kinematics layer to ensure collision avoidance, improve safety, and maintain task performance in cluttered environments.

Contribution

It presents a novel application of control barrier functions at the IK layer for shared autonomy, providing hard safety guarantees while preserving task effectiveness.

Findings

Reduced collision violation time in simulation

Increased minimum clearance in cluttered environments

Participants preferred safety-enhanced shared autonomy in VR study

Abstract

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.