Keisuke Ito

Tokyo Institute of Technology, University of Electro-Communications

Papers

Total Citations

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About

Keisuke Ito is a roboticist whose work focuses on the challenges of efficient, adaptive, and underactuated robotic systems. His research spans the design of minimalist locomotion, intelligent mobile manipulation, and the application of reinforcement learning to complex control problems. Ito’s most cited work, "Underactuated crawling robot" (2002, 12 citations), addresses a fundamental trade-off in robotics: reducing actuator count to minimize size and weight while overcoming the inherent difficulty of controlling such systems. He further advanced the field of mobile manipulation with "A planning method for efficient mobile manipulation considering ambiguity" (2012, 3 citations), proposing a system that enables robots to navigate more effectively toward target objects by accounting for the limited reach of robotic arms. Additionally, his study on designing controllers using reinforcement learning with an adaptive state recruitment strategy (2003, 2 citations) tackles the critical issue of high-dimensional, continuous state spaces that plague real-world robot learning. Ito’s contributions are particularly valuable for researchers interested in resource-constrained robotics, where efficiency and adaptability are paramount. His work provides foundational insights into creating robots that are both simpler in design and more capable in complex, ambiguous environments.