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About

Nattapon Jaisumroum is a robotics researcher specializing in decentralized control systems, neural network-based learning, and multi-robot cooperation. His work focuses on enabling robot manipulators to collaboratively perform dynamic tasks, such as balancing objects on a flat plate, through adaptive, intelligent control. His most-cited paper, “Self-tuning control with neural network for robot manipulator” (2016, 8 citations), introduces a neural network-driven self-tuning controller for a 3DOF haptic robot arm, demonstrating real-time adaptation in object balancing. Jaisumroum’s major contribution lies in developing a decentralized learning framework where each robot independently adjusts its neural network parameters—using backpropagation—to achieve coordinated motion without centralized supervision. This approach is detailed in his conceptual framework paper (2016, 3 citations) and further validated in later works (2019, 3 citations; 2017, 2 citations). By integrating neural network control with decentralized architectures, Jaisumroum advances scalable, robust solutions for cooperative robotics, with potential applications in manufacturing, logistics, and human-robot interaction. His work represents a meaningful step toward autonomous multi-robot systems capable of handling complex, real-time physical tasks.