Nikolaos Tziortziotis
Papers
1
Total Citations
3
H-Index
1
About
Nikolaos Tziortziotis is a researcher whose work sits at the intersection of reinforcement learning, robotics, and autonomous navigation, with a particular focus on complex, over-actuated systems. His key contributions center on developing intelligent motion planning algorithms that enable robotic platforms to operate efficiently under real-world constraints, including environmental disturbances and measurement noise. In his highly cited 2018 paper, "Motion Planning with Energy Reduction for a Floating Robotic Platform Under Disturbances and Measurement Noise Using Reinforcement Learning," Tziortziotis introduced an innovative online least-squares policy iteration scheme for value function approximation. This work demonstrated how reinforcement learning could be used to navigate an over-actuated marine platform—a system with more control inputs than degrees of freedom—in unknown environments while simultaneously reducing energy consumption. By tackling the dual challenges of uncertainty and energy efficiency, his research has provided a practical framework for autonomous marine robotics, earning recognition from the community with 3 citations. Tziortziotis’s contributions are particularly valuable for students and researchers interested in applying reinforcement learning to real-world robotic systems, where robustness and adaptability are critical.
Research Focus
Key Achievements
Top Papers
- 1