Publikation
A Safety-Aware Shared Autonomy Framework with BarrierIK Using Control Barrier Functions
Berk Guler; Kay Pompetzki; Yuanzheng Sun; Simon Manschitz; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2603.01705, Pages 1-9, arXiv, 2026.
Zusammenfassung
Shared autonomy blends operator intent with
autonomous assistance. In cluttered environments, linear blend-
ing 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 sim-
ulation 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 BarrierIK.