Vision-based Target Tracking for a Skid-steer Vehicle using Guided Policy Search with Field-of-view Constraint

This paper describes a vision-based target tracking method for a skid-steer vehicle. With the development of deep reinforcement learning, many researchers have tried to generate an end-to-end policy to control the mobile robot from a raw pixel image data. However, the action in most research only concerns high-level decisions such as go straight, turn left and right. High-level decisions alone are not sufficient to precisely control platforms such as a skid-steer vehicle due to the lack of steering mechanism. Thus, unlike existing work, we aim to control the motor command for the wheels directly. To this end, we employ guided policy search (GPS) based on the general kinematic slip model for the skid-type robot. Furthermore, to prohibit the target from getting out of the camera field of view (FOV) in the training phase, we update local policy optimization with a FOV constraint and perform a pre-training to make the initial policy more efficient. Our method allows the skid-type robot to automatically acquire the vision-based tracking policy while local policies satisfy the FOV constraint during the training phase. We evaluate our method through both simulation and experiment with a skid-steer mobile robot. Finally, we test the performance of learned policy with a moving target in a new environment.