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