The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology.