Octavian Melinte
Institute of Solid Mechanics, Romanian Academy, Springer Nature (Germany)
Papers
13
Total Citations
269
H-Index
7
About
Octavian Melinte is a leading researcher at the intersection of robotics, artificial intelligence, and human-robot interaction. His work focuses on equipping robots with advanced perception and control capabilities, particularly through deep learning and intelligent interfaces. Melinte’s most impactful contribution is in facial expression recognition for human-robot interaction, where he developed an end-to-end pipeline using deep convolutional neural networks (CNNs) optimized with the Rectified Adam optimizer—a paper cited 99 times. He has also made significant strides in environmental robotics, authoring a highly cited study (91 citations) on real-time waste identification using CNN-based object detectors, demonstrating the practical application of AI in sustainability. His research extends to haptic interfaces for rescue robots, fuzzy logic control for modular robots, and the development of digital twins for Industry 4.0, including a robot digital twin for high-frequency hardening. With over 250 total citations across his publications, Melinte’s work is characterized by its blend of theoretical rigor and real-world deployment, notably on platforms like the NAO robot, making him a key figure in advancing intelligent, autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Haptic interfaces for the rescue walking robots motion in the disaster areas17 citations · 2014
- 4Dynamic analysis for the leg mechanism of a wheel-leg hybrid rescue robot11 citations · 2014
- 5Fuzzy dynamic modeling for walking modular robot control11 citations · 2010
- 6
- 7Robot Digital Twin towards Industry 4.07 citations · 2020
- 8Haptic intelligent interfaces for NAO robot hand control6 citations · 2015
- 9Digital Twin in 5G Digital era developed through Cyber Physical Systems6 citations · 2020
- 10NAO robot fuzzy obstacle avoidance in virtual environment5 citations · 2019