Papers
8
Total Citations
90
H-Index
4
About
Koji Ishihara is a robotics researcher whose work centers on the control and actuation of humanoid and bio-inspired robotic systems, with a particular focus on optimal control, model predictive control (MPC), and pneumatically actuated muscles (PAMs). His most influential contribution is a full-body optimal control framework for humanoid robots that enables versatile and agile behaviors, a paper that has garnered 39 citations and represents a significant step toward real-time whole-body motion generation. Ishihara has also advanced hybrid actuator strategies, such as combining artificial muscles with electric motors to mimic human joint efficiency, and developed human-in-the-loop control methods for PAM-based robots, addressing the challenge of compliant, biologically inspired actuation. His work on computationally efficient MPC, including a two-step optimization approach for real-time humanoid control, has further pushed the boundaries of practical model-based control. Notably, his recent development of a split-force-controlled body weight support robot for gait rehabilitation demonstrates a direct application of his expertise in PAMs and force control to assistive healthcare technology. With a publication record spanning from 2014 to 2023, Ishihara’s research consistently bridges theoretical control advances with real-world robotic systems, making him a notable figure in the field of humanoid and compliant robotics.
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- 8CPG-BASED LOCOMOTION LEARNING OF FOUR-LEGGED ROBOT BY MULTI-OBJECTIVE GA2 citations · 2014