Papers
71
Total Citations
786
H-Index
14
About
Yasutake Takahashi is a prominent robotics researcher whose work spans reinforcement learning, autonomous robot behavior acquisition, and human-robot interaction. Best known for his pioneering contributions to making reinforcement learning practical for real-world robotic systems, Takahashi tackled fundamental challenges in state space construction and continuous-valued learning environments — problems that had long limited the deployment of intelligent robots outside controlled laboratory settings. His influential 2002 paper on incremental state space segmentation (70 citations) and subsequent work on continuous-valued Q-learning (48 citations) demonstrated that robots could achieve reasonable performance with significantly reduced learning time, a breakthrough for adaptive robotic systems. His multi-layered reinforcement learning framework further advanced the field by enabling modular, transferable knowledge across tasks. Beyond learning algorithms, Takahashi contributed to physical robot design, developing a six-limbed robot integrating locomotion and manipulation (38 citations) and RFID-based indoor localization systems. His participation in RoboCup 2005 (61 citations) highlights his engagement with competitive, real-world robotics benchmarks. More recently, his research on humanoid robots and emotional expression in prisoner's dilemma games (31 citations) reflects a compelling evolution toward understanding human-robot social dynamics. With over 385 total citations, Takahashi's career represents a sustained and multifaceted contribution to intelligent, adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2RoboCup 2005: Robot Soccer World Cup IX61 citations · 2006
- 3Continuous valued Q-learning for vision-guided behavior acquisition48 citations · 2003
- 4
- 5A mobile robot testbed with manipulator for security guard application35 citations · 2002
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- 9Multi-controller fusion in multi-layered reinforcement learning25 citations · 2002
- 10Incremental State Space Segmentation for Behavior Learning by Real Robot.23 citations · 1999