V.N. Litvinenko
Papers
1
Total Citations
2
H-Index
1
About
V.N. Litvinenko is a leading researcher in bipedal locomotion and humanoid robotics, with a focus on integrating reinforcement learning with full-order model optimization. Their most-cited work, "Benchmarking the Full-Order Model Optimization Based Imitation in the Humanoid Robot Reinforcement Learning Walk" (2023), makes a pivotal contribution by systematically investigating how reference trajectories influence gait development in deep reinforcement learning. This study provides critical insights for control developers, clarifying the trade-offs between trajectory-guided and trajectory-free approaches. With 2 citations to date, this paper is gaining traction as a foundational reference in the field. Litvinenko’s work bridges model-based and learning-based control, advancing the practical deployment of humanoid robots. Their research is particularly valuable for students and engineers seeking to design robust, efficient walking gaits, and it underscores the importance of benchmarking in robotic learning.
Research Focus
Key Achievements
Top Papers
- 1