Emanuil Huluta

Papers

Total Citations

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About

Emanuil Huluta is a researcher in robotics and human-robot interaction, with a focus on tactile sensing, neural network control, and dexterous manipulation. His work explores how robotic systems can interpret complex sensory data—particularly kinaesthetic and tactile information—to perform tasks that mimic human touch and hand control. In his most-cited paper, “Data-driven analysis of kinaesthetic and tactile information for shape classification” (2015, 9 citations), Huluta investigates how biological touch systems integrate multiple sensory inputs without conscious prioritization, applying data-driven methods to enable shape recognition in robotic hands. His second notable work, “Neural network-Based hand posture control of a humanoid Robot Hand” (2014, 4 citations), addresses the challenge of achieving human-like, trainable control in multi-finger robotic hands through neural network architectures. Though his citation counts reflect a developing career, Huluta’s contributions are significant for advancing intuitive, sensor-rich robotic manipulation. His research bridges biomechanics and artificial intelligence, offering insights for prosthetics, industrial automation, and humanoid robotics. For students and researchers, Huluta’s work highlights the importance of integrating tactile feedback and adaptive control in building more capable, human-like robotic systems.