Luca Pulina
Papers
6
Total Citations
59
H-Index
6
About
Luca Pulina is a leading researcher at the intersection of formal verification, robotics, and artificial intelligence, with a primary focus on ensuring the safety and effectiveness of autonomous systems. His work addresses the critical challenge of deploying learning-enabled robots in real-world environments where unsafe behavior must be demonstrably minimized. Pulina's most cited paper, "Ensuring safety of policies learned by reinforcement: Reaching objects in the presence of obstacles with the iCub" (2013, 17 citations), introduces a framework for verifying that stochastic policies learned through reinforcement maintain a low collision probability, using the humanoid iCub robot as a case study. He has also made significant contributions to multi-agent control systems, as seen in his work on safe and effective learning (2010, 8 citations), and to the application of probabilistic model checking for robot control policies (2016, 6 citations). Additionally, his research on SMT-based planning for robots in smart factories (2019, 6 citations) and consistency of property specification patterns (2018, 14 citations) demonstrates a sustained commitment to bridging theoretical verification methods with practical robotic applications. Pulina's work is essential for researchers and engineers seeking to build trustworthy autonomous systems that can operate safely alongside humans.
Research Focus
Key Achievements
Top Papers
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- 3Safe and effective learning: A case study8 citations · 2010
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- 6SMT-based Planning for Robots in Smart Factories6 citations · 2019