PUBLICATIONS
Journal Articles

  • Shun Hiramatsu and Shingo Murata, “Goal-conditioned self-supervised learning from play aimed at human–robot collaboration,” Submitted, 2024.

  • Kentaro Fujii, Takuya Isomura, and Shingo Murata, “Real-World Robot Control Based on Contrastive Deep Active Inference with Demonstrations,” IEEE Access, Early Access, pp. 1–15, 2024. DOI: 10.1109/ACCESS.2024.3477306

  • Takafumi Soda, Shingo Murata, Asako Toyama, Shinsuke Suzuki, Yoshihiko Kunisato, Kentaro Katahira, and Yuichi Yamashita, “Psychometric Properties of Hierarchical Psychiatric Symptoms on the General Population,” Submitted, 2024. PsyArXiv

  • Tadahiro Taniguchi, Shingo Murata, Masahiro Suzuki, Dimitri Ognibene, Pablo Lanillos, Emre Ugur, Lorenzo Jamone, Tomoaki Nakamura, Alejandra Ciria, Bruno Lara, and Giovanni Pezzulo, “World models and predictive coding for cognitive and developmental robotics: frontiers and challenges,” Advanced Robotics, pp. 780–806, 2023. DOI: 10.1080/01691864.2023.2225232 arXiv
    Advanced Robotics Best Survey Paper Award

  • Yuta Takahashi, Shingo Murata, Masao Ueki, Hiroaki Tomita, and Yuichi Yamashita, “Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data,” Computational Psychiatry, 7(1), pp. 14–29, 2023. DOI: 10.5334/cpsy.93

  • Taisuke Kobayashi, Shingo Murata, and Tetsunari Inamura, “Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics,” IEEE Transactions on Human-Machine Systems, Vol. 52, Issue 5, pp. 928–940, 2022. DOI: 10.1109/THMS.2022.3182909 arXiv / Movie

  • Namiko Saito, Tetsuya Ogata, Hiroki Mori, Shingo Murata, and Shigeki Sugano, “Tool-use Model to Reproduce the Goal Situations Considering Relationship among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks,” Frontiers in Robotics and AI, Vol. 8, Article 748716, pp. 1–15, 2021. DOI: 10.3389/frobt.2021.748716

  • Yuta Takahashi, Shingo Murata, Hayato Idei, Hiroaki Tomita, and Yuichi Yamashita, “Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework,” Scientific Reports, Vol. 11, Article number: 14684, pp. 1–14, 2021. DOI: 10.1038/s41598-021-94067-x / PsyArXiv
    Press Release: Keio Univ. (in Japanese) / Tohoku Univ. (in English)

  • Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder,” Neural Networks, Vol. 138, pp. 150–163, 2021. DOI: 10.1016/j.neunet.2021.01.033

  • Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder,” Frontiers in Psychiatry, Vol. 11, Article 762, pp. 1–15, 2020. DOI: 10.3389/fpsyt.2020.00762
    Press Release (in Japanese): NCNP / Waseda Univ.

  • 出井勇人, 村田真悟, 尾形哲也, 山下祐一, “不確実性の推定と自閉スペクトラム症－神経ロボティクス実験による症状シミュレーション,” 精神医学, Vol. 62, No. 2, pp. 219–229, 2020. DOI: 10.11477/mf.1405206009

  • Hayato Idei, Shingo Murata, Yiwen Chen, Yuichi Yamashita, Jun Tani, and Tetsuya Ogata, “A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision,” Computational Psychiatry, Vol. 2, pp. 164–182, 2018. DOI: 10.1162/cpsy_a_00019

  • Ryoichi Nakajo, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Acquisition of Viewpoint Transformation and Action Mappings via Sequence to Sequence Imitative Learning by Deep Neural Networks,” Frontiers in Neurorobotics, Vol. 12, Article 46, pp. 1–14, 2018. DOI: 10.3389/fnbot.2018.00046

  • Shingo Murata, Yuxi Li, Hiroaki Arie, Tetsuya Ogata, and Shigeki Sugano, “Learning to Achieve Different Levels of Adaptability for Human–Robot Collaboration Utilizing a Neuro-dynamical System,” IEEE Transactions on Cognitive and Developmental Systems, Vol. 10, Issue 3, pp. 712–725, 2018. DOI: 10.1109/TCDS.2018.2797260

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions,” Frontiers in Neurorobotics, Vol. 11, Article 70, pp. 1–18, 2017. DOI: 10.3389/fnbot.2017.00070

  • Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, and Jun Tani, “Learning to Perceive the World as Probabilistic or Deterministic via Interaction with Others: A Neuro-Robotics Experiment,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, Issue 4, pp. 830–846, 2017. DOI: 10.1109/TNNLS.2015.2492140

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction,” Frontiers in Neurorobotics, Vol. 10, Article 5, pp. 1–17, 2016. DOI: 10.3389/fnbot.2016.00005

  • Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, and Jun Tani, “Learning to Generate Proactive and Reactive Behavior Using a Dynamic Neural Network Model with Time-Varying Variance Prediction Mechanism,” Advanced Robotics, Vol. 28, Issue 17, pp. 1189–1203, 2014. DOI: 10.1080/01691864.2014.916628

  • Shingo Murata, Jun Namikawa, Hiroaki Arie, Shigeki Sugano, and Jun Tani, “Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning via Tutoring,” IEEE Transactions on Autonomous Mental Development, Vol. 5, Issue 4, pp. 298–310, 2013. DOI: 10.1109/TAMD.2013.2258019

International Conference Papers

  • Gabriel W. Haddon-Hill and Shingo Murata, “Active Vision for Physical Robots using the Free Energy Principle,” In Proceedings of the 33rd International Conference on Artificial Neural Networks (ICANN 2024), pp. 455–460, Oral Presentation, Lugano, Switzerland, September 2024. DOI: 10.1007/978-3-031-72359-9_20

  • Ko Igari, Kentaro Fujii, Gabriel W. Haddon-Hill, and Shingo Murata, “Selection of Exploratory or Goal-Directed Behavior by a Physical Robot Implementing Deep Active Inference,” The 5th International Workshop on Active Inference (IWAI 2024), Oral Presentation, Oxford, UK, September 2024.

  • Kaito Kusumoto and Shingo Murata, “Toward Understanding Psychiatric and Cognitive Characteristics: A Deep Generative Model for Extracting Shared and Private Representations and Its Evaluation with Synthetic Multimodal Data,” In Proceedings of the 13th IEEE International Conference on Development and Learning (ICDL 2023), pp. 455–460, Oral Presentation (Acceptance Rate: 65%), Macau, China, November 2023. DOI: 10.1109/ICDL55364.2023.10364479

  • Keigo Ishii, Shun Hiramatsu, Yuta Nomura, and Shingo Murata, “Goal-Conditioned Flexible Object Manipulation by Self-Supervised Learning from Play,” In Proceedings of the 13th IEEE International Conference on Development and Learning (ICDL 2023), pp. 150–155, Oral Presentation (Acceptance Rate: 65%), Macau, China, November 2023. DOI: 10.1109/ICDL55364.2023.10364471

  • Kentaro Fujii and Shingo Murata, “Hierarchical Latent Dynamics Model with Multiple Timescales for Learning Long-Horizon Tasks,” In Proceedings of the 13th IEEE International Conference on Development and Learning (ICDL 2023), pp. 479–485, Oral Presentation (Acceptance Rate: 65%), Macau, China, November 2023. DOI: 10.1109/ICDL55364.2023.10364442

  • Yuta Nomura and Shingo Murata, “Real-World Robot Control and Data Augmentation by World-Model Learning from Play,” In Proceedings of the 13th IEEE International Conference on Development and Learning (ICDL 2023), pp. 133–138, Oral Presentation (Acceptance Rate: 65%), Macau, China, November 2023. DOI: 10.1109/ICDL55364.2023.10364556

  • Shun Hiramatsu and Shingo Murata, “Deep Predictive Network for Inference and Dynamic Optimization of Task Goals during Human–Robot Collaboration,” In Proceedings of the 2023 IEEE International Joint Conference on Neural Networks (IJCNN 2023), 6 pages, Poster Presentation (Acceptance Rate: 54.76%), Gold Coast, Australia, June 2023. DOI: 10.1109/IJCNN54540.2023.10191733

  • Namiko Saito, Joao Moura, Tetsuya Ogata, Marina Aoyama, Shingo Murata, Shigeki Sugano, and Sethu Vijayakumar, “Structured Motion Generation with Predictive Learning: Proposing Subgoal for Long-Horizon Manipulation,” In Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), pp. 9566–9572, Accepted (Acceptance Rate: 43.04%), London, UK, May-June 2023. DOI: 10.1109/ICRA48891.2023.10161046

  • Shingo Murata, Wataru Masuda, Jiayi Chen, Hiroaki Arie, Tetsuya Ogata, and Shigeki Sugano “Achieving Human–Robot Collaboration with Dynamic Goal Inference by Gradient Descent,” In Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), pp. 579–590, Oral Presentation (Acceptance Rate: 27.4%), Sydney, Australia, December 2019. DOI: 10.1007/978-3-030-36711-4_49

  • Shingo Murata, Hikaru Yanagida, Kentaro Katahira, Shinsuke Suzuki, Tetsuya Ogata, and Yuichi Yamashita, “Large-scale Data Collection for Goal-directed Drawing Task with Self-report Psychiatric Symptom Questionnaires via Crowdsourcing,” In Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2019), pp. 3839–3845, Oral Presentation (Acceptance Rate: 60.6%), Bari, Italy, October 2019. DOI: 10.1109/SMC.2019.8914041

  • Shingo Murata, Hiroki Sawa, Shigeki Sugano, and Tetsuya Ogata, “Looking Back and Ahead: Adaptation and Planning by Gradient Descent,” In Proceedings of the Ninth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2019), pp. 151–156, Oral Presentation (Acceptance Rate for Oral Presentation: 28%), Oslo, Norway, August 2019. DOI: 10.1109/DEVLRN.2019.8850693
    Travel Grant from the Hara Research Foundation (Shingo Murata)

  • Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata, and Shigeki Sugano, “Tool-use Model Considering Tool Selection by a Robot using Deep Learning,” In Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids 2018), pp. 814–819, Oral Presentation (Acceptance Rate for Oral Presentation: 19.5%), Beijing, China, November 2018. DOI: 10.1109/HUMANOIDS.2018.8625048

  • Yuheng Wu, Kuniyuki Takahashi, Hiroki Yamada, Kitae Kim, Shingo Murata, Shigeki Sugano, and Tetsuya Ogata, “Dynamic Motion Generation by Flexible-Joint Robot based on Deep Learning using Images,” In Proceedings of the Eighth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2018), pp. 169–174, Poster Presentation, Tokyo, Japan, September 2018. DOI: 10.1109/DEVLRN.2018.8761020

  • Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata, and Shigeki Sugano, “Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning,” In Proceedings of the Eighth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2018), pp. 91–96, Poster Presentation, Tokyo, Japan, September 2018. DOI: 10.1109/DEVLRN.2018.8761029

  • Udara Manawadu, Takahiro Kawano, Shingo Murata, Mitsuhiro Kamezaki, Junya Muramatsu, and Shigeki Sugano, “Multiclass Classification of Driver Perceived Workload Using Long Short-Term Memory based Recurrent Neural Network,” In Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV'18), pp. 2009–2014, Poster Presentation, Changshu, China, June 2018. DOI: 10.1109/IVS.2018.8500410

  • Udara Manawadu, Takahiro Kawano, Shingo Murata, Mitsuhiro Kamezaki, and Shigeki Sugano, “Estimating Driver Workload with Systematically Varying Traffic Complexity Using Machine Learning: Experimental Design,” In Proceedings of the 2018 International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems (iHSI 2018), Oral Presentation, pp. 106–111, Dubai, UAE, January 2018. DOI: 10.1007/978-3-319-73888-8_18

  • Hayato Idei, Shingo Murata, Yiwen Chen, Yuichi Yamashita, Jun Tani, and Tetsuya Ogata, “Reduced Behavioral Flexibility by Aberrant Sensory Precision in Autism Spectrum Disorder: A Neurorobotics Experiment,” In Proceedings of the Seventh Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2017), pp. 271–276, Oral Presentation (Acceptance Rate for Oral Presentation: 37.1%), Lisbon, Portugal, September 2017. DOI: 10.1109/DEVLRN.2017.8329817

  • Shingo Murata, Wataru Masuda, Saki Tomioka, Tetsuya Ogata, and Shigeki Sugano, “Mixing Actual and Predicted Sensory States based on Uncertainty Estimation for Flexible and Robust Robot Behavior,” In Proceedings of the 26th International Conference on Artificial Neural Networks (ICANN 2017), pp. 11–18, Oral Presentation (Acceptance Rate: 47.4%), Alghero, Italy, September 2017. DOI: 10.1007/978-3-319-68600-4_2
    Travel Grant from the Hara Research Foundation (Shingo Murata)

  • Shingo Murata, Kai Hirano, Hiroaki Arie, Shigeki Sugano, and Tetsuya Ogata, “Analysis of Imitative Interactions between Humans and a Robot with a Neuro-dynamical System,” In Proceedings of the 2016 IEEE/SICE International Symposium on System Integration (SII 2016), pp. 343–348, Oral Presentation (Acceptance Rate: 78.9%), Hokkaido, Japan, December 2016. DOI: 10.1109/SII.2016.7844022 / Movie

  • Ryoichi Nakajo, Maasa Takahashi, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Self and Non-self Discrimination Mechanism Based on Predictive Learning with Estimation of Uncertainty,” In Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP 2016), pp. 228–235, Poster Presentation (Acceptance Rate: 68.7%), Kyoto, Japan, October 2016. DOI: 10.1007/978-3-319-46681-1_28

  • Yuxi Li, Shingo Murata, Hiroaki Arie, Tetsuya Ogata, and Shigeki Sugano, “Achieving Different Levels of Adaptability for Human–Robot Collaboration Utilizing a Neuro-Dynamical System,” Workshop on Bio-inspired Social Robot Learning in Home Scenarios, The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016), 6 pages, Poster Presentation, Daejeon, Korea, October 2016. PDF

  • Yiwen Chen, Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Jun Tani, and Shigeki Sugano, “Emergence of Interactive Behaviors between Two Robots by Prediction Error Minimization Mechanism,” In Proceedings of the Sixth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2016), pp. 302–307, Oral Presentation (Acceptance Rate for Oral Presentation: 34%), Cergy-Pontoise, France, September 2016. DOI: 10.1109/DEVLRN.2016.7846838 / Movie
    Travel Grant from the Hara Research Foundation (Yiwen Chen)

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Dynamical Linking of Positive and Negative Sentences to Goal-oriented Robot Behavior by Hierarchical RNN,” In Proceedings of the 25th International Conference on Artificial Neural Networks (ICANN 2016), pp. 339–346, Oral Presentation, Barcelona, Spain, September 2016. DOI: 10.1007/978-3-319-44778-0_40
    Best Paper Award / Travel Grant from the Telecommunications Advancement Foundation (Tatsuro Yamada)

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Attractor Representations of Language–behavior Structure in a Recurrent Neural Network for Human–robot Interaction,” In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), pp. 4179–4184, Oral Presentation (Acceptance Rate: 46%), Hamburg, Germany, September 2015. DOI: 10.1109/IROS.2015.7353968
    Travel Grant from the Hara Research Foundation (Tatsuro Yamada)

  • Shingo Murata, Saki Tomioka, Ryoichi Nakajo, Tatsuro Yamada, Hiroaki Arie, Tetsuya Ogata, and Shigeki Sugano, “Predictive Learning with Uncertainty Estimation for Modeling Infants’ Cognitive Development with Caregivers: A Neurorobotics Experiment,” In Proceedings of the Fifth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2015), pp. 302–307, Oral Presentation, Providence, USA, August 2015. DOI: 10.1109/DEVLRN.2015.7346162

  • Ryoichi Nakajo, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Acquisition of Viewpoint Representation in Imitative Learning from Own Sensory-Motor Experiences,” In Proceedings of the Fifth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2015), pp. 326–331, Oral Presentation, Providence, USA, August 2015. DOI: 10.1109/DEVLRN.2015.7346166

  • Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Jun Tani, and Shigeki Sugano, “Generation of Sensory Reflex Behavior versus Intentional Proactive Behavior in Robot Learning of Cooperative Interactions with Others,” In Proceedings of the Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2014), pp. 242–248, Oral Presentation (Acceptance Rate for Oral Presentation: 28%), Genoa, Italy, October 2014. DOI: 10.1109/DEVLRN.2014.6982988

  • Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Jun Tani, and Shigeki Sugano, “Learning and Recognition of Multiple Fluctuating Temporal Patterns Using S-CTRNN,” In Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN 2014), pp. 9–16, Oral Presentation (Acceptance Rate: 62%), Hamburg, Germany, September 2014). DOI: 10.1007/978-3-319-11179-7_2 / Movie
    Travel Grant from the Hara Research Foundation (Shingo Murata)

  • Kuniyuki Takahashi, Tetsuya Ogata, Hadi Tjandra, Shingo Murata, Hiroaki Arie, and Shigeki Sugano, “Tool-body Assimilation Model based on Body Babbling and a Neuro-dynamical System for Motion Generation,” In Proceedings of the 24th International Conference on Artificial Neural Networks (ICANN 2014), pp. 363–370, Oral Presentation (Acceptance Rate: 62%), Hamburg, Germany, September 2014. DOI: 10.1007/978-3-319-11179-7_46

  • Shingo Murata, Jun Namikawa, Hiroaki Arie, Jun Tani, and Shigeki Sugano, “Development of Proactive and Reactive Behavior via Meta-Learning of Prediction Error Variance,” In Proceedings of the 20th International Conference on Neural Information Processing (ICONIP 2013), pp. 537–544, Oral Presentation, Deagu, Korea, November 2013. DOI: 10.1007/978-3-642-42054-2_67

  • Shingo Murata, Jun Namikawa, Hiroaki Arie, Jun Tani, and Shigeki Sugano, “Learning to Reproduce Fluctuating Behavioral Sequences Using a Dynamic Neural Network Model with Time-Varying Variance Estimation Mechanism,” In Proceedings of the Third Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2013), pp. 1–6, Poster Presentation (Acceptance Rate: 67%), Osaka, Japan, August 2013. DOI: 10.1109/DevLrn.2013.6652545

International Conference Abstracts

  • Kentaro Fujii, Takuya Isomura, and Shingo Murata, “Deep Active Inference with Reconstructive and Contrastive Learning,” The 5th International Workshop on Active Inference (IWAI 2024), Poster Presentation, Oxford, UK, September 2024.

  • Ryusei Murata, Yuta Takahashi, Yuichi Yamashita, and Shingo Murata, “Hypernetwork-Based Integrative Behavioral Modeling: Extracting Latent States from fMRI and Decision-Making Task Data,” Poster Presentation, Computational Psychiatry Conference, Minnesota, USA, July 2024.

  • Kentaro Fujii, Takuya Isomura, and Shingo Murata, “Real-World Robot Control Based on Contrastive Active Inference with Learning from Demonstration,” The 4th International Workshop on Active Inference (IWAI 2023), Poster Presentation, Ghent, Belgium, September 2023.

  • Kentaro Fujii and Shingo Murata, “Multiple Timescale Recurrent State-Space Model for Learning Long-Horizon Tasks,” International Symposium on Predictive Brain and Cognitive Feelings, Poster Presentation, Tokyo, Japan, July 2023.

  • Kaito Kusumoto and Shingo Murata, “A Deep Generative Model for Extracting Shared and Private Latent Representations from Multimodal Data,” International Symposium on Predictive Brain and Cognitive Feelings, Poster Presentation, Tokyo, Japan, July 2023.

  • Yukiko Orui and Shingo Murata, “Action Modification Based on Real-time Amortized Inference of Others’ Intentions Using Backward RNN,” The 54th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS '22), Oral Presentation (Online) , Nara, Japan, October 2022.

  • Shingo Murata, Kai Hirano, Naoto Higashi, Shin-ichiro Kumagaya, Yuichi Yamashita, and Tetsuya Ogata, “Analysis of Imitative Interactions between Typically Developed or Autistic Participants and a Robot with a Recurrent Neural Network,” The Ninth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2019), Poster Presentation, Oslo, Norway, August 2019.

  • Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Altered Sense of Self Induced by Functional Disconnection in a Hierarchical Neural Network: A Neuro-Robotics Study,” International Consortium on Hallucination Research and Related Symptoms Kyoto Satellite Meeting (ICHR 2018 KYOTO), Poster Presentation, Kyoto, Japan, October 2018.

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Representation Learning of Logical Words via Seq2seq Learning from Linguistic Instructions to Robot Actions,” Workshop on Representation Learning for Human and Robot Cognition, The 5th International Conference on Human–Agent Interaction (HAI 2017), Oral Presentation, Bielefeld, Germany, October 2017.

  • Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Altered Behavioral Flexibility and Generalization Induced by Reduced Heterogeneity of Intrinsic Neuronal Excitability: A Neurorobotics Study,” WPA XVII World Congress of Psychiatry, Oral Presentation, Berlin, Germany, October 2017.

  • Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Logically Complex Symbol Grounding for Interactive Robots by Seq2seq Learning with an LSTM-RNN,” The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS 2016), Demonstration (Acceptance Rate for Demonstration: 36.4%), Barcelona, Spain, December 2016.

  • Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Jun Tani, and Shigeki Sugano, “Neuro-Dynamical Accounts for Postdiction,” The 19th Annual Meeting of the Association for the Scientific Study of Consciousness (ASSC 19), Poster Presentation, Paris, France, July 2015.

  • Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Jun Tani, and Shigeki Sugano, “Self-Organization of Distinct Neural Mechanisms for Adaptive Behavior,” Neurobiologically Inspired Robotics Workshop: Incorporating Brain Processing into Robots Might for Better Autonomy, The 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Oral Presentation, Hong Kong, China, June 2014.

  • Shingo Murata, Yuichi Yamashita, Tetsuya Ogata, Hiroaki Arie, Jun Tani, and Shigeki Sugano, “Altered Prediction of Uncertainty Induced by Network Disequilibrium: A Neuro-Robotics Study,” Poster Presentation, Computational Psychiatry 2013, Miami, USA, October 2013.

Book

  • Tetsuya Ogata, Kuniyuki Takahashi, Tatsuro Yamada, Shingo Murata, and Kazuma Sasaki, “Machine Learning for Cognitive Robotics,” Cognitive Robotics, Angelo Cangelosi and Minoru Asada (Eds.), The MIT Press, 2022. URL: https://mitpress.mit.edu/books/cognitive-robotics

TOP