Koki Kage
Papers
2
Total Citations
15
H-Index
2
About
Koki Kage is a pioneering researcher in swarm robotics and multi-robot systems, with a focus on bio-inspired task allocation and autonomous specialization. Their most-cited work, “Response threshold-based task allocation in a reinforcement learning robotic swarm” (2014, 13 citations), applies the response threshold model observed in insect societies to enable division of labor in robotic swarms. This foundational contribution demonstrates how social insect intelligence can be translated into scalable, decentralized control for multi-robot systems performing collective tasks. Kage further advanced the field with “Robust reinforcement learning technique with bigeminal representation of continuous state space for multi-robot systems” (2012, 2 citations), introducing Bayesian-discrimination-function-based reinforcement learning (BRL). This technique allows robots to autonomously segment continuous state spaces and specialize without human intervention—a novel concept in cooperative robotics. By bridging biological principles with machine learning, Kage’s work provides a framework for adaptive, self-organizing robot teams. Their research is essential reading for students and engineers interested in swarm intelligence, distributed AI, and the future of autonomous multi-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2