Liudas Petrauskas
Papers
1
Total Citations
4
H-Index
1
About
Liudas Petrauskas is a researcher at the forefront of intelligent robotics and autonomous systems, with a particular focus on enhancing the precision and adaptability of articulated robots. His most notable contribution, detailed in the 2020 paper "Improving Positioning Accuracy of an Articulated Robot Using Deep Q-Learning Algorithms," demonstrates a pioneering application of reinforcement learning to real-world robotic control. By leveraging Deep Q-Learning, Petrauskas developed a method that significantly refines the positioning accuracy of multi-jointed robots—a critical challenge in manufacturing, assembly, and precision automation. This work, which has garnered 4 citations, stands out for its practical integration of advanced AI algorithms with traditional robotic hardware, offering a scalable solution to improve performance without costly mechanical upgrades. Petrauskas’s research bridges the gap between theoretical machine learning and industrial robotics, showcasing how deep reinforcement learning can optimize complex, non-linear systems. His contributions are particularly valuable for students and engineers seeking to understand how AI-driven approaches can solve longstanding problems in robotic motion control, making his work a key reference in the evolving field of intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1