Navigation of inertial forces driven mini-robots using reinforcement learning

In this paper we propose a reinforcement learning (RL) framework for the autonomous navigation of a pair of mini-robots that are driven by inertial forces. The inertial forces are provided by two vibration motors on each mini-robot which are controlled by a simple and efficient low-level speed controller. The action of the RL agent is the direction of the velocity of each mini-robot, and it based on the position of each mini-robot, the distance between the mini-robots, and the sign of the distance gradient. Each mini-robot is considered as a moving obstacle to the other that must by avoided. We have introduced a suitable reward function that results into an efficient collaborative RL approach. A simulation environment is created using the ROS framework, that include the dynamic model of the mini-robot and of the vibration motors. Several application scenarios are simulated, and the presented results demonstrate the performance of the proposed framework.