Arif Nugroho
Papers
2
Total Citations
11
H-Index
2
About
Arif Nugroho is a robotics researcher whose work centers on control systems, multi-agent coordination, and data-driven modeling for robotic manipulators. His research bridges classical control theory with modern machine learning, particularly in the context of humanoid robots. In his 2019 paper, "Cooperative Multi-agent for The End-Effector Position of Robotic Arm Based on Consensus and PID Controller" (6 citations), Nugroho addressed the challenge of synchronizing multiple robotic agents with random initial states, proposing a structured consensus-based framework to achieve cooperative end-effector positioning. This work laid a foundation for managing decentralized robotic systems. More recently, Nugroho contributed a significant open-source resource with his 2023 paper, "ARKOMA dataset: An open-source dataset to develop neural networks-based inverse kinematics model for NAO robot arms" (5 citations). By providing a curated dataset specifically for the NAO humanoid robot, he enables researchers to train flexible neural network models for inverse kinematics—a critical component for smooth, accurate motion planning. This dataset helps democratize access to high-quality training data, accelerating progress in data-driven robotics. Nugroho’s work is notable for its practical, open-source ethos and its integration of multi-agent control with neural network approaches, making him a valuable contributor to the fields of cooperative robotics and humanoid motion control.
Research Focus
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Top Papers
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