Francesco Tassi
Papers
15
Total Citations
106
H-Index
5
About
Francesco Tassi is a robotics researcher whose work centers on advanced control frameworks for human-robot collaboration, teleoperation, and adaptive robot manipulation. His primary contributions lie in developing Hierarchical Quadratic Programming (HQP) methodologies that enable robots to simultaneously manage multiple competing objectives — from task execution and physical constraints to human ergonomics and safety. Tassi's most influential work, "An Adaptive Compliance Hierarchical Quadratic Programming Controller for Ergonomic Human-Robot Collaboration" (2022, 28 citations), exemplifies his signature approach: embedding human-centric parameters directly into optimization-based control architectures. His Augmented HQP framework, introduced in 2021 and expanded across several publications, has become a recurring foundation in his research, enabling robots to respond intelligently to dynamic, contact-rich environments shared with human collaborators. Beyond collaboration, Tassi has tackled challenges in tele-locomanipulation, impact planning, learning from demonstration, and non-prehensile object catching — reflecting a broad yet coherent research vision. His 2021 reconfigurable tele-operation interface work (18 citations) further demonstrates his commitment to operator wellbeing alongside system performance. With over 90 cumulative citations across a focused body of work, Tassi is establishing himself as a meaningful contributor to intelligent, ergonomic, and adaptive robot control — particularly relevant for next-generation industrial and collaborative robotics applications.
Research Focus
Key Achievements
Top Papers
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- 2A Reconfigurable Interface for Ergonomic and Dynamic Tele-Locomanipulation18 citations · 2021
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