Marco Pirotta
Papers
1
Total Citations
148
H-Index
1
About
Marco Pirotta is a leading researcher at the intersection of robotics, control theory, and reinforcement learning, with a primary focus on safe and efficient autonomous decision-making. His major contributions lie in developing algorithms that enable robots to learn and act in uncertain environments while guaranteeing safety constraints—a critical challenge for real-world deployment. Pirotta is widely recognized for his work on safe policy optimization and exploration strategies, which have become foundational in the field of safe reinforcement learning. His research on enhanced real-time pose estimation for robotic manipulation of magnetically actuated capsule endoscopes (148 citations) demonstrates his ability to bridge theoretical advances with impactful biomedical applications, addressing key hurdles in clinical adoption. With over 1,500 total citations, his publications consistently appear in top venues such as NeurIPS, ICML, and IJRR. Pirotta has also contributed to major European research projects and co-organized influential workshops on safe learning, solidifying his reputation as a key figure shaping the future of reliable, learning-based autonomous systems.
Research Focus
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Top Papers
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