Reinforcement Learning Motion Planning for an EOGcentered 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 modelfree 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 userxe2x80x99s intent, environment perception and previous machinebased decisions. In order to prevent collisions, machinebased 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 inhouse 3D virtual model of our Research Center (ISRUC). The experimental results show a better performance of the proposed approach with RL when compared to the version without the RLbased motion planning component. Results show that the approach is a promising step in the concept put forward for collaborative HumanRobotic Interaction (HRI), and opens a path for future research.

Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning The majority of learning from demonstration approaches do not address suboptimal demonstrations or cases when drastic changes in the environment occur after the demonstrations were made. For example, in real teleoperation tasks, the demonstrations provided by the user are often suboptimal due to interface and hardware limitations. In tasks involving comanipulation and manipulation planning, the environment often changes due to unexpected obstacles rendering previous demonstrations invalid. This paper presents a reinforcement learning algorithm that exploits the use of relevance functions to tackle such problems. This paper introduces the Pearson correlation as a measure of the relevance of policy parameters in regards to each of the components of the cost function to be optimized. The method is demonstrated in a static environment where the quality of the teleoperation is compromised by the visual interface (operating a robot in a threedimensional task by using a simple 2D monitor). Afterward, we tested the method on a dynamic environment using a real 7DoF robot arm where distributions are computed online via Gaussian Process regression.