Algirdas Petronis
Papers
4
Total Citations
41
H-Index
3
About
Algirdas Petronis is a robotics researcher focused on advancing the precision and automation of industrial and construction machinery. His primary research areas include deep reinforcement learning, machine learning for robotic control, and sensor-based positioning systems. Petronis’s major contribution lies in pioneering the application of Deep Q-Learning algorithms to significantly improve the positioning accuracy and repeatability of articulated industrial robots—a critical factor in modern manufacturing. His most cited work, “Deep Q-Learning in Robotics: Improvement of Accuracy and Repeatability” (2022), has garnered 30 citations, establishing a foundation for integrating AI into precision control. Beyond stationary robots, he has also advanced automation in heavy machinery, developing methods for determining excavator tool positions using absolute sensors, which supports trajectory generation and simulation. Through a series of papers from 2020 to 2022, Petronis has demonstrated how modern machine learning can add substantial value to existing equipment, enhancing competitiveness and paving the way for smarter, more autonomous industrial systems.
Research Focus
Key Achievements
Top Papers
- 1Deep Q-Learning in Robotics: Improvement of Accuracy and Repeatability30 citations · 2022
- 2Determination of Excavator Tool Position using Absolute Sensors5 citations · 2021
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