Principled Authority Switching for Shared Autonomy in Human-Robot Teams

Abstract

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the human's likelihood of intervention. This paper presents Flip-Team, a cooperative framework for authority switching in shared autonomy. We formulate the control switching problem as a team-optimal decision problem in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We model the human's override propensity, the likelihood of exercising override authority, and derive a critical threshold that determines when human intervention is cost-effective. For linear-quadratic systems, we derive closed-form switching conditions and value function recursions, enabling efficient computation independent of the continuous state dimension. We further propose an online method to estimate override propensity from observed interventions, enabling adaptive switching without prior calibration. Evaluation on a quadrotor altitude regulation task demonstrates that Flip-Team achieves lower cost than always-human and always-autonomous baselines, with selective authority switching that balances human adaptability and autonomous efficiency.