Papers
87
Total Citations
1,053
H-Index
17
About
Sho Sakaino is a robotics researcher whose work spans bipedal locomotion, haptic communication, force control, and robot learning — areas that collectively address some of the most challenging problems in intelligent and interactive robotics. His early contributions to biped robot locomotion, particularly the "virtual slope method" for stair walking trajectory planning (91 citations) and a three-mass model approach for real-time 3D walking generation (66 citations), established him as a significant voice in humanoid robot motion planning. His research then broadened into haptic teleoperation, where he tackled the problem of bilateral control between structurally dissimilar systems, including electric and hydraulic actuators, enabling more versatile and realistic remote manipulation with a sense of touch. A particularly notable thread in his later work is the fusion of bilateral control with machine learning: his imitation learning framework using force and position data (51 citations) and its extension with autoregressive methods demonstrate a forward-thinking integration of data-driven techniques with precise physical interaction. His reinforcement learning approach for contact-rich assembly tasks and development of a high dynamic range force sensor further reflect his commitment to bridging perception, control, and intelligence in robotic systems with real-world applicability.
Research Focus
Key Achievements
Top Papers
- 1Walking Trajectory Planning on Stairs Using Virtual Slope for Biped Robots91 citations · 2010
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- 10Development of a physical therapy robot for rehabilitation databases25 citations · 2012