Papers
20
Total Citations
685
H-Index
11
About
Giovanni De Magistris is a leading researcher at the intersection of robotics, deep reinforcement learning (DRL), and industrial automation. His most impactful work demonstrates how robots can overcome physical precision limits using intelligent algorithms. His seminal paper, "Deep Reinforcement Learning for High Precision Assembly Tasks" (300 citations), pioneered the use of DRL to enable robots to perform tight-clearance peg-in-hole tasks without tedious manual parameter tuning—a breakthrough for manufacturing. To make DRL safe for real-world deployment, he introduced "OptLayer" (118 citations), a constrained optimization framework that prevents unsafe trial-and-error interactions. De Magistris has also advanced sim-to-real transfer learning, using variational autoencoders to bridge the gap between synthetic training data and real-world vision (38 citations), and developed unsupervised anomaly detection systems for industrial robots (45 citations). His work extends beyond factories to underwater surveillance, where he has contributed to cooperative autonomy for networked AUVs. With over 600 total citations, De Magistris’s research is essential reading for anyone working on practical, safe, and precise robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1Deep reinforcement learning for high precision assembly tasks300 citations · 2017
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- 5Deep Reinforcement Learning for High Precision Assembly Tasks29 citations · 2017
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- 10Optimized humanoid walking with soft soles12 citations · 2017