Yuta Tsuboi
Papers
3
Total Citations
236
H-Index
3
About
Yuta Tsuboi is a leading researcher at the intersection of robotics, natural language processing, and human-in-the-loop reinforcement learning. His work focuses on enabling robots to understand and act upon unconstrained spoken language instructions—a critical step toward seamless human-robot collaboration. Tsuboi’s most influential contribution, “Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions” (2018), has garnered 175 citations and addresses the formidable challenges of parsing complex, ambiguous speech in real-world robotic manipulation. By developing systems that comprehend diverse spoken expressions, he has advanced the practical deployment of interactive robots. In parallel, his work “DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback” (2018, 48 citations) tackles the exploration problem in RL, integrating human feedback to accelerate learning and reduce the trial burden in robotic applications. Tsuboi’s research is notable for bridging the gap between theoretical RL and tangible robotic tasks, making it highly relevant for students and engineers aiming to build more intuitive, responsive machines. His achievements underscore a commitment to creating robots that can listen, learn, and act in dynamic, human-centric environments.
Research Focus
Key Achievements
Top Papers
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