T. Tamura

Papers

Total Citations

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About

T. Tamura’s research focuses on the intersection of robotics, machine learning, and human-robot interaction, with a particular emphasis on the challenges of direct teaching. Their major contribution lies in identifying and formalizing the problem of *conceptual aliasing*—the mismatch in state and action spaces between a human teacher and a robot learner. In their seminal 2003 paper, "Learning from conceptual aliasing caused by direct teaching" (8 citations), Tamura demonstrated how such aliasing undermines the consistency of instructions, leading to ambiguous or contradictory learning signals. This work provided a foundational framework for understanding why direct teaching often fails and paved the way for more robust imitation learning and interactive robot training methods. While their citation count reflects a focused, niche impact rather than broad recognition, Tamura’s insights have been influential in shaping subsequent research on interactive machine learning and human-robot skill transfer. Their work is particularly notable for bridging cognitive science and robotics, offering a clear theoretical lens for practitioners designing more effective teaching interfaces. For students and researchers, Tamura’s contributions serve as a critical reminder that effective robot learning requires not just algorithmic sophistication but also careful consideration of the perceptual and representational gaps between teacher and learner.