Motoaki Hiraga
Papers
16
Total Citations
125
H-Index
6
About
Motoaki Hiraga is a robotics researcher specializing in swarm robotics, evolutionary computation, and deep reinforcement learning, with a particular focus on enabling autonomous collective behavior in multi-robot systems. His work addresses one of the field's central challenges: how groups of simple robots with only local sensory capabilities can accomplish complex tasks without centralized control. Hiraga's most influential contribution, "Autonomous Task Allocation by Artificial Evolution for Robotic Swarms in Complex Tasks" (2018, 22 citations), demonstrated how evolutionary algorithms can spontaneously generate role specialization within robot swarms — a capability inspired by biological systems such as ant colonies. This theme of autonomous specialization recurs throughout his research, including studies examining how congestion affects swarm performance and how robots can evolve distinct behavioral roles in path-formation scenarios. More recently, Hiraga has advanced the application of deep reinforcement learning to swarm control, exploring deep Q-learning for end-to-end policy generation and extending swarm robotics beyond conventional wheeled platforms to multi-legged robots capable of navigating uneven terrain. His investigations into topology-evolving neural networks further demonstrate his commitment to adaptive, scalable controller design. With over 100 cumulative citations, his body of work represents a meaningful contribution to building smarter, more versatile robotic swarms.
Research Focus
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