Papers
48
Total Citations
1,356
H-Index
20
About
Eiji Uchibe is a pioneering robotics and machine learning researcher whose career spans over two decades of foundational work in reinforcement learning, autonomous robot behavior, and intelligent systems. His research has consistently focused on enabling robots to learn adaptive, complex behaviors in real-world environments — from vision-based reinforcement learning for mobile robots in the late 1990s to cutting-edge deep reinforcement learning applications in manipulation and navigation. Uchibe's most influential contribution, "Deep Reinforcement Learning with Smooth Policy Update" (2018, 179 citations), demonstrated how high-dimensional sensory inputs could guide robots through intricate tasks like cloth manipulation. His early work on cooperative behavior acquisition (1999, 144 citations) and modular reinforcement learning (2002, 93 citations) established elegant frameworks for decomposing complex tasks into learnable subtasks — approaches that anticipated modern hierarchical learning paradigms. His involvement in the Cyber Rodent Project explored the fascinating intersection of artificial life and adaptive robotics, probing the origins of reward and motivation systems. With over 840 cumulative citations, Uchibe's contributions have shaped how researchers approach scalable robot learning, multi-agent coordination, and the integration of intrinsic motivation into reinforcement learning — making him an essential figure for anyone studying autonomous systems and embodied AI.
Research Focus
Key Achievements
Top Papers
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- 8Constrained reinforcement learning from intrinsic and extrinsic rewards49 citations · 2007
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