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About

Gianpaolo Gulletta is a robotics researcher whose work sits at the intersection of human movement science and autonomous systems, with a particular focus on generating naturalistic, biologically-inspired motion for humanoid robots. His most influential contribution, a comprehensive review of human-like arm motion generation (2020), has accumulated over 70 citations across multiple venues and stands as a key reference for researchers navigating the rapidly evolving landscape of biologically-inspired motion planning techniques. Building on this foundational survey, Gulletta developed a practical upper-limb motion planner capable of producing naturalistic arm movements for humanoid robots, work that has garnered 35 citations and directly addresses the challenge of social acceptance in human-robot interaction. His earlier research tackled the complexity of bimanual coordination, applying nonlinear optimization to synchronize dual arm-hand robotic systems during cooperative tasks — a technically demanding problem with meaningful implications for collaborative robotics. Across his body of work, Gulletta consistently bridges theoretical neuroscience-inspired models and real-world robotic implementation, making him a notable contributor to the field of socially intelligent, human-compatible robot motion design.