Supporting impaired people with a following robotic assistant by means of end-to-end visual target navigation and reinforcement learning approaches

We present an improvement in visual object tracking and navigation for mobile robot implementing the advantage actor-critic (A2C) reinforcement learning architecture on top of the Gym-Gazebo framework. This work provides an easier way to integrate reinforcement learning algorithms for navigation and object tracking tasks in robotics field. We train the convolutional-recurrent model employed for the policy estimation in an end-to-end manner. The robot is able to follow a simulated human walking in an indoor environment by using the sequence of images provided by the robot camera. The input of the algorithm is acquired and processed directly in ROS-Gazebo environment. The policy learned by the robot agent proved to generalize well also in an environment with different size and shape with respect to the training one. Moreover, the policy allows the robot to avoid obstacles while following the tracking target. Thanks to these improvements, we can straightforwardly apply the tracking system in a real world robot for a person following task in indoor environments.