Reinforcement Learning Motion Planning for an EOG-centered Robot Assisted Navigation in a Virtual Environment

This paper presents a new collaborative approach for robot motion planning of an assistive robotic platform that takes into account the intentions of the user provided through Electrooculographic (EOG) signals, as well as obstacles surrounding the robotic platform. In order to increase human confidence in the operation of robotic platforms with some degree of navigational autonomy, the intent of the user must be included in the decision process. In our system, the humanrobot interface works through ocular movements (saccades and blinks), which are acquired as EOG signals and classified using a Convolutional Neural Network. In our proposed approach, a model-free Reinforcement Learning (RL) layer is used to provide commands to a virtual robotic platform. The RL layer is constantly being updated with the inputs from the user’s intent, environment perception and previous machine-based decisions. In order to prevent collisions, machine-based perception using the proposed RL motion planning approach will assist the user by selecting suitable actions while learning from prior driving behaviors. The approach was validated by a set of tests that consisted of driving a robotic platform in an in-house 3D virtual model of our Research Center (ISR-UC). The experimental results show a better performance of the proposed approach with RL when compared to the version without the RL-based motion planning component. Results show that the approach is a promising step in the concept put forward for collaborative Human-Robotic Interaction (HRI), and opens a path for future research.