Generation of compensation behavior of autonomous robot for uncertainty of information with probabilistic flow control

Abstract

We exhibit that a robot shows strategic behaviors for overcoming uncertainty of information in navigation tasks by a modification of the core formula in Q MDP value method. The new formula biases the calculation so as to give a priority, the decision-making to an important part of the state space. Q MDP value method with the modified formula is named as probabilistic flow control (PFC) method. We apply PFC method to a simulated simple task of a mobile robot. In the task, the robot with PFC method shows tricky behaviors that compensate the uncertainty of state information.