Chizuko Mishima

Papers

Total Citations

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About

Chizuko Mishima’s research lies at the intersection of robotics, machine learning, and human-robot interaction, with a particular focus on enabling robots to learn autonomously from human instruction. Her most influential work addresses the fundamental challenge of “cross perceptual aliasing”—the mismatch between how a teacher and a learner perceive the same environment. In her 2003 paper, Mishima proposed an active learning method that empowers robots to self-improve by asking targeted questions, effectively bridging the gap between direct teaching and autonomous understanding. This contribution, cited 6 times, remains a thoughtful exploration of how robots can become more adaptive learners. Earlier, Mishima contributed to the RoboCup initiative, co-authoring papers on vision-based robot learning for the Osaka University “Trackies” (8 citations) and the BabyTigers-98 legged robot team (3 citations). These works helped lay the groundwork for competitive, real-world robot learning. While her citation counts are modest, Mishima’s research offers valuable insights for students and researchers interested in active learning, perceptual alignment, and the practical challenges of teaching robots in dynamic environments.