Daniel Melesse

Hartford Financial Services (United States)

Papers

Total Citations

H-Index

About

Daniel Melesse is a researcher in robotics and haptic feedback, with a focus on teleoperation and human-robot interaction. His work centers on extending human control to challenging environments through robotic proxies, addressing key challenges in joint-limit avoidance and sensorimotor feedback. His most-cited paper, "Sampling of 3DOF Robot Manipulator Joint-Limits for Haptic Feedback" (2019, 4 citations), introduces a novel sampling-based method to provide haptic cues that help operators avoid joint limits during teleoperation, enhancing safety and precision in remote manipulation. This contribution is critical for applications in hazardous or inaccessible settings, such as disaster response or space exploration, where robots must operate within physical constraints while maintaining intuitive human control. Melesse’s research bridges the gap between robotic autonomy and human dexterity, demonstrating how haptic feedback can improve operator awareness and task performance. Though early in his career, his work has already influenced discussions on teleoperation robustness and user interface design, marking him as a promising voice in the field of robotic manipulation and human-robot collaboration.