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About

Djitt Laowattana is a pioneering roboticist whose research bridges intelligent control, humanoid robotics, and force-guided manipulation. His most influential work introduces a modified fuzzy Q-learning algorithm for mobile robots, incorporating a reward-sharing mechanism that dramatically accelerates learning—a contribution that has garnered 26 citations and remains foundational in reinforcement learning for autonomous systems. Laowattana also advanced neuro-fuzzy control for bipedal locomotion, developing algorithms that enable humanoid robots to achieve stable, dynamic walking; his 2008 paper on this topic has been cited 17 times. He led the ambitious FIBO humanoid robot project, resulting in the creation of "Somjuk" (FHR-1), a lower-body anthropomorphic platform with custom mechanisms and controllers, detailed in an 8-citation study. Beyond walking, Laowattana explored expertness metrics for knowledge sharing among robots and applied multi-objective genetic algorithms to optimize fast walking gaits. His earlier work on force-feedback-based dual peg insertion and impact force control demonstrates deep expertise in contact-rich manipulation. With over 60 total citations across these core papers, Laowattana’s research has shaped both the theory and practice of intelligent robotics, from learning and control to physical humanoid design.