The COVID-19 pandemic has highlighted the role of telepresence systems, yet manually controlling remote robots often falls short for complex manipulation tasks. To address this challenge, a machine learning-based assistive manipulation approach has been developed. This innovative method identifies target objects and computes an inverse kinematic solution for grasping them, seamlessly integrating the solution with the user’s arm movements at various inverse kinematic (IK) fusion levels.

Recognizing the importance of maintaining a sense of body ownership over the remote robot, the study explores how haptic feedback and assistive functions influence ownership perception and task performance. The findings reveal that incorporating assistance and haptic feedback significantly enhances robotic arm control in telepresence environments, resulting in improved precision and reduced task completion times. This research highlights the benefits of assistive manipulation techniques and haptic feedback in advancing telepresence technology.