  Trajectory and control secrecy is an important issue in robotics security.
This paper proposes a novel algorithm for the control input inference of a
mobile agent without knowing its control objective. Specifically, the algorithm
first estimates the target state by applying external perturbations. Then we
identify the objective function based on the inverse optimal control, providing
the well-posedness proof and the identifiability analysis. Next, we obtain the
optimal estimate of the control horizon using binary search. Finally, the
agent's control optimization problem is reconstructed and solved to predict its
input. Simulation illustrates the efficiency and the performance of the
algorithm.
