Shohei Hama
Papers
1
Total Citations
2
H-Index
1
About
Shohei Hama is a robotics researcher whose work focuses on advancing machine learning techniques for dynamic system modeling, particularly in the context of robotic manipulation and control. His key contributions center on the dynamics learning tree (DLT), a powerful framework for capturing complex, nonlinear behaviors in robotic systems. In his most-cited paper, "Effective input order of dynamics learning tree" (2018, 2 citations), Hama addresses a critical practical challenge: optimizing the sequence of input data to improve DLT’s learning performance when applied to robot arms. This work builds on prior applications of DLT to boats, vehicles, and humanoid robots, but Hama’s targeted approach enhances its efficiency and accuracy for real-world robotic tasks. While his citation count is modest, his research is notable for bridging theoretical machine learning with hands-on robotics, offering a method that can reduce computational overhead and improve model reliability. Hama’s contributions are valuable for students and engineers seeking to implement adaptive control systems in robotics, as they provide a clear, practical pathway for optimizing learning algorithms in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Effective input order of dynamics learning tree2 citations · 2018