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About

A. Tascillo’s research lies at the intersection of robotics, intelligent control, and neural-fuzzy systems, with a primary focus on dexterous robotic hand control. Tascillo’s most significant contributions involve developing hybrid neurofuzzy algorithms that enable robotic hands to learn efficient, pressure-sensitive grasps for applications such as wheelchair-mounted assistive arms. By combining the adaptive learning of neural networks with the rule-based reasoning of fuzzy logic, Tascillo created controllers that improve grasp stability through tip and slip sensory feedback. This work, detailed in papers such as “Neural and fuzzy robotic hand control” (17 citations) and “Intelligent control of a robotic hand with neural nets and fuzzy sets” (8 citations), established a foundation for more intuitive and responsive prosthetic and assistive robotics. Tascillo also explored broader control challenges, including coordinated dual-hand manipulation using stochastic Petri nets and neural planning, as well as redundant robotic trajectory optimization with diagnostic motor control. Additionally, Tascillo extended neurofuzzy methods to automotive applications, developing a hierarchical anticipatory neural controller with fuzzy spectral filtering for engine and chassis dynamometer modeling. Though citation counts are modest, Tascillo’s work demonstrates a pioneering integration of soft computing techniques for real-time, adaptive robotic control.