I.K. Minashina
Papers
1
Total Citations
2
H-Index
1
About
I.K. Minashina is a robotics researcher whose work focuses on the intersection of reinforcement learning and bipedal locomotion, particularly for humanoid robots. Her key research areas include imitation learning, full-order model optimization, and the development of stable walking gaits. Her most cited paper, "Benchmarking the Full-Order Model Optimization Based Imitation in the Humanoid Robot Reinforcement Learning Walk" (2023), systematically investigates how reference trajectories influence the performance of deep reinforcement learning for bipedal gait control. This work provides critical insights for control developers, weighing the trade-offs between using and omitting reference trajectories. With 2 citations, this study is a foundational contribution to the field, offering a benchmark for future research in humanoid robot walking. Minashina’s research is notable for its practical focus on optimizing gait stability and efficiency, making her a rising voice in robotics. Her work is especially valuable for students and researchers exploring model-based versus model-free approaches in robot control, as she clarifies the nuanced advantages of each method in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1