Papers
7
Total Citations
85
H-Index
4
About
Stefano Baraldo is a robotics researcher whose work spans industrial robot motion planning, human-robot collaboration, and intelligent manufacturing systems. He has established himself as a versatile contributor to both classical robotics and the emerging demands of Industry 5.0. Baraldo's most influential contribution — his 2017 paper on smooth trajectory generation for high-precision industrial assembly, which has accumulated 54 citations — laid important groundwork for enabling robots to perform delicate tasks with the accuracy that modern manufacturing requires. Complementing this, his work on reconfigurable robot manipulators addressed a key barrier to modular robotics adoption in industry. More recently, Baraldo has shifted focus toward human-centered robotics, exploring how robots can meaningfully collaborate with human workers in complex, unstructured environments. His research has tackled stress detection through multimodal sensing, robust speech recognition for smart manufacturing, and personalized motion planning using dynamic movement primitives. Particularly forward-looking is his use of federated learning to evaluate worker mental states while preserving privacy — a timely response to Industry 5.0's emphasis on worker well-being. Across his career, Baraldo's work reflects a coherent vision: building robotic systems that are not only precise and adaptive, but genuinely responsive to the humans working alongside them.
Research Focus
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