Control Input Inference of Mobile Agents under Unknown Objective

TL;DR

This paper introduces a new algorithm to infer the control inputs of mobile agents without prior knowledge of their objectives, enhancing security analysis in robotics.

Contribution

It presents a novel inverse optimal control-based method for control input inference, including estimation, objective identification, and horizon determination.

Findings

Algorithm effectively predicts control inputs in simulations.

The method demonstrates high accuracy and efficiency.

The approach is theoretically validated with proofs.

Abstract

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.