Hamdan, SaraÖztop, ErhanUğurlu, Regaip Barkan2020-12-082020-12-082019-10978-1-7281-4569-31062-922Xhttp://hdl.handle.net/10679/7170https://doi.org/10.1109/SMC.2019.8914009In this paper, a method to control a manipulator using force-induced trajectory is proposed. The trajectory is learned from an operator doing the polishing task using a tool attached to the robot's end-effector. The learning process is performed by a deep neural network which is designed and trained to generate a force profile according to the states (joints' positions and velocities). The admittance control technique is utilized to make the manipulator compliant to the operator movements in the teaching mode. Spring-Damper system along with Inertia-Damper system has been studied to impose the relationship between the operator's applied force and the reaction of the manipulator. The universal robot (UR5) aside with a force sensor (OptoForce) are used to run the experiment. Robot Operation System (ROS) is used to accomplish the task in real-time. The polishing task is learned and achieved by the robot itself, and the force trajectories are better followed using the Inertia-Damper system as the admittance controlling scheme.engopenAccessconferenceObject4019402400052135390400810.1109/SMC.2019.8914009Admittance controlPhysical human-robot interactionIntelligent robot control2-s2.0-85076770205