Luca Puricelli
Papers
2
Total Citations
6
H-Index
2
About
Luca Puricelli is a robotics researcher whose work focuses on the critical challenge of enabling robots to interact safely and precisely with their environments. His primary research areas lie at the intersection of **interaction control**, **model predictive control**, and **reinforcement learning** for robotic manipulation. Puricelli’s major contributions include the development of an optimized residual action framework that allows robots to learn and adapt to unknown environments during physical interaction, significantly improving both safety and force-tracking accuracy. His work on an actor-critic model predictive force controller provides a data-driven solution to the long-standing problem of manual controller tuning in industrial settings, automating the process of achieving precise force exertion. While his most-cited papers, including his 2023 work on optimized residual action (4 citations) and his experimental validation of a learning-based force controller (2 citations), are early in their citation lifecycle, they represent foundational steps toward more intelligent and autonomous industrial robots. Puricelli’s research promises to make robots more capable of performing complex assembly, polishing, and human-robot collaboration tasks without requiring exhaustive manual programming.
Research Focus
Key Achievements
Top Papers
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- 2