Publications

Yohei Hayamizu (PhD student)

Autonomous Intelligent Robotics Lab. Department of Computer Science, SUNY Binghamton.

Research Area:

Artificial Intelligence, Robotics, Reinforcement Learning, Dialog Navigation Systems

Contact:

yhayami1 "at" binghamton.edu

For a complete list of publications, visit my Google Scholar profile.

Xiaohan Zhang, Yan Ding, Yohei Hayamizu, Zainab Altaweel, Yifeng Zhu, Yuke Zhu, Peter Stone, Chris Paxton, Shiqi Zhang (*Equal Contribution): LLM-GROP: Visually Grounded Robot Task and Motion Planning with Large Language Models, The International Journal of Robotics Research (IJRR), 2025. (Paper)
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama, Keiki Takadama, Hisao Ishibuchi, and Masaya Nakata: Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems, IEEE Transactions on Evolutionary Computation (TEVC), 2025. (Paper)

Yohei Hayamizu, David DeFazio, Hrudayangam Mehta, Zainab Altaweel, Jacqueline Choe, Chao Lin, Jake Juettner, Furui Xiao, Jeremy Blackburn, Shiqi Zhang: From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication, AAAI, 2026. (Project)

Xiaohan Zhang, Zainab Altaweel, Yohei Hayamizu, Yan Ding, Saeid Amiri, Hao Yang, Andy Kaminski, Chad Esselink, and Shiqi Zhang (*Equal Contribution): DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning, AAAI LM4Plan Workshop, 2025. (Paper, Project)

Xiaohan Zhang, Zainab Altaweel, Yohei Hayamizu, Yan Ding, Saeid Amiri, Hao Yang, Andy Kaminski, Chad Esselink, and Shiqi Zhang (*Equal Contribution): DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning, CVPR EAI Workshop, 2024. (Paper)
David DeFazio, Yohei Hayamizu, and Shiqi Zhang: Learning quadruped locomotion policies using logical rules, International Conference on Automated Planning and Scheduling (ICAPS), 2024. (Paper, Project)
Issei Saito, Tomoaki Nakamura, Akira Taniguchi, Tadahiro Taniguchi, Yohei Hayamizu, and Shiqi Zhang: Emergence of continuous signals as shared symbols through emergent communication, IEEE International Conference on Development and Learning (ICDL), 2024. (Paper)

Yohei Hayamizu, Zhou Yu, and Shiqi Zhang: Learning Joint Policies for Human-Robot Dialog and Co-Navigation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023. (Paper)
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama: Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems, Genetic and Evolutionary Computation Conference (GECCO), 2023. (Paper)

Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama (*Equal Contribution): Beta Distribution-based XCS Classifier System, IEEE Congress on Evolutionary Computation (CEC), 2022. (Paper)
Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama: Can the same rule representation change its matching area? enhancing representation in XCS for continuous space by probability distribution in multiple dimension, Genetic and Evolutionary Computation Conference (GECCO), 2022. (Paper)
Hiroki Shiraishi, Yohei Hayamizu, Hiroyuki Sato, Keiki Takadama: Absumption based on overgenerality and condition-clustering based specialization for XCS with continuous-valued inputs, Genetic and Evolutionary Computation Conference (GECCO), 2022. (Paper) 🏆 Best Paper Award (EML Track)
Hiroki Shiraishi, Yohei Hayamizu, Iko Nakari, Hiroyuki Sato, Keiki Takadama: Inheritance vs. Expansion: Generalization Degree of Nearest Neighbor Rule in Continuous Space as Covering Operator of XCS, International Conference on the Applications of Evolutionary Computation, 2022. (Paper)

Yohei Hayamizu, Saeid Amiri, Kishan Chandan, Keiki Takadama, Shiqi Zhang: Guiding Robot Exploration in Reinforcement Learning via Automated Planning, International Conference on Automated Planning and Scheduling (ICAPS), 2021. (Paper, Video, Code)
Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, and Keiki Takadama: Misclassification Detection based on Conditional VAE for Rule Evolution in Learning Classifier System, Genetic and Evolutionary Computation Conference (GECCO), 2021. (Paper)
Hiroki Shiraishi, Masakazu Tadokoro, Yohei Hayamizu, Yukiko Fukumoto, Hiroyuki Sato, and Keiki Takadama: Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System, IEEE Congress on Evolutionary Computation (CEC), 2021. (Paper)

Yohei Hayamizu, Saeid Amiri, Kishan Chandan, Keiki Takadama, Shiqi Zhang: Efficient Exploration in Reinforcement Learning Leveraging Automated Planning, NeurIPS Workshop on Robot Learning, 2020. (Paper, Video)

Cover Feature, Watson Review – Recognized for significant contributions to the robotic project (Summer 2025)
GECCO Best Paper Award (EML Track) – 2022
UEC Meguro-kai Award – Top 5% of students, University of Electro-Communications (2021)
UEC President’s Award for Students – Top 10% of students, University of Electro-Communications (2021)
SSI Excellent Paper Award – 2020
FIT Best Paper Award – 2020
ARLISS UNISEC Award – Most challenging CanSat mission (2018)
Iwate University Kusakari Award – Top 5% of students, Iwate University (2018)