Torki Altameem
Papers
2
Total Citations
26
H-Index
2
About
Dr. Torki Altameem is a distinguished researcher whose work bridges the frontiers of robotics, artificial intelligence, and healthcare. His primary research areas include underactuated robotic systems, adaptive neuro-fuzzy methodologies, and the application of deep reinforcement learning in mental health interventions. Dr. Altameem’s major contributions include pioneering the use of adaptive neuro-fuzzy techniques to identify the most strained joints in underactuated robotic fingers, a critical advancement for designing more efficient and durable prosthetic and industrial manipulators. His highly cited 2014 paper on this topic has garnered 15 citations, establishing a foundational framework for robotic joint optimization. More recently, Dr. Altameem has broken new ground by developing a deep reinforcement learning process for robotic training to assist mental health patients, a 2020 work with 11 citations that exemplifies his commitment to socially impactful technology. This innovative approach demonstrates how AI-driven robotics can provide therapeutic support, marking a significant step toward intelligent, empathetic machines. Through these achievements, Dr. Altameem has not only advanced theoretical understanding in robotics and AI but also opened promising avenues for their real-world application in improving human well-being.
Research Focus
Key Achievements
Top Papers
- 1
- 2