Muhammad Arshad Khan
Papers
1
Total Citations
14
H-Index
1
About
Muhammad Arshad Khan is an emerging researcher at the intersection of robotics, machine learning, and healthcare technology, with a particular focus on autonomous medical robotics and intelligent motion planning. His most notable contribution lies in the development of **Deep Movement Primitives**, a novel framework that enables robots to autonomously perform complex, geometry-adaptive tasks such as breast cancer examination through palpation. This work addresses a critical challenge in medical robotics — programming robots to handle variable anatomical geometries — representing a meaningful step toward accessible, automated cancer screening solutions with the potential for global health impact. Published in 2022 and already accumulating 14 citations, this research demonstrates both technical innovation and real-world clinical relevance. Khan's work bridges the gap between traditional movement primitive approaches and deep learning architectures, allowing robotic systems to generalize learned skills across diverse physical configurations. His contributions are particularly significant given the worldwide prevalence of breast cancer and the urgent need for scalable diagnostic tools. Researchers and students interested in medical robotics, imitation learning, or human-robot interaction will find Khan's work a compelling foundation for advancing autonomous healthcare systems.
Research Focus
Key Achievements
Top Papers
- 1Deep Movement Primitives: Toward Breast Cancer Examination Robot14 citations · 2022