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

2026-03-02

AI Summary

This work addresses a critical safety issue in shared control: linear blending of human intent and autonomous assistance can yield unsafe commands even when each component is individually safe, yet existing approaches often rely on soft constraints without rigorous safety guarantees. To overcome this limitation, the paper introduces the first integration of Control Barrier Functions (CBFs) into the inverse kinematics (IK) layer of shared autonomy, enforcing hard safety constraints post-fusion while preserving task performance. User studies conducted in both simulation and virtual reality teleoperation demonstrate that the proposed method significantly reduces collision duration, increases minimum obstacle clearance, enhances users’ perceived safety and trust, decreases perceived interference, and achieves higher user preference compared to baseline approaches.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• shared autonomy
• safety guarantee
• control barrier functions
• collision avoidance
• inverse kinematics

Innovation

• Control Barrier Functions
• Shared Autonomy
• Inverse Kinematics
• Safety-Critical Control
• BarrierIK