Carmadi Machbub
Bandung Institute of Technology, Informatics Institute of Technology
Papers
17
Total Citations
190
H-Index
8
About
Carmadi Machbub is a prominent robotics and human-computer interaction researcher whose work sits at the intersection of intelligent systems, autonomous robotics, and multimodal communication. Based on a consistently cited body of scholarship, Machbub has made significant contributions to humanoid robot control, gesture and speech recognition, and computer vision-based tracking. Among his most impactful contributions is his research into natural human-robot interaction, including hand gesture recognition using K-means clustering and Support Vector Machines (41 citations) and the integration of Indonesian speech and gesture recognition for robot control — work that notably addresses accessibility needs for deaf and elderly users. His development of path planning algorithms for humanoid home-assistant robots using A-Star search (36 citations) demonstrates a strong commitment to practical autonomous navigation solutions. Machbub has also advanced humanoid locomotion through Kalman Filter and PD Controller-based gait systems, and explored inverse kinematics for gesture imitation on NAO robots. More recently, his deep learning-enhanced object tracking research reflects a natural evolution toward computer vision applications in surveillance and healthcare robotics. Collectively accumulating over 160 citations, his work offers enduring value to researchers building intelligent, human-centered robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Hand gesture recognition using K-means clustering and Support Vector Machine41 citations · 2018
- 2Humanoid Robot Path Planning and Rerouting Using A-Star Search Algorithm36 citations · 2019
- 3Gait Controllers on Humanoid Robot Using Kalman Filter and PD Controller15 citations · 2018
- 4Deep features fusion for KCF-based moving object tracking14 citations · 2023
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- 8Centroid-Tracking-Aided Robust Object Detection for Hospital Objects8 citations · 2020
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