Yuya Koga
Papers
9
Total Citations
62
H-Index
5
About
Yuya Koga is a leading researcher in the field of musculoskeletal humanoids—robots that closely mimic the human musculoskeletal system. His work centers on exploiting the inherent redundancy of these complex, tendon-driven platforms to achieve robust, adaptive, and safe behavior. A key contribution is the development of online learning frameworks that allow these robots to compensate for muscle rupture, a critical failure mode, by dynamically reconfiguring their control strategies using redundant intersensory networks. This work, detailed in his most-cited paper (16 citations), demonstrates a path toward truly resilient hardware. Koga has also pioneered the application of these robots to autonomous driving, a landmark achievement summarized in a 2020 article (14 citations) that showcases the potential of flexible, human-like bodies for complex environmental interaction. His research further spans design optimization to maximize redundancy for fail-safe operation (7 citations), self-body image acquisition for dexterous manipulation (7 citations), and biomimetic control that integrates muscle activation with joint nullspace optimization (5 citations). Through these efforts, Koga is systematically addressing the fundamental challenges of controlling highly redundant, soft-bodied robots, pushing the boundaries of what is possible in biomimetic robotics and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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