Embedded Files
Journal
Journal
Yoshihisa Tsurumine, Yunduan Cui, Eiji Uchibe, and Takamitsu Matsubara. “Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation.” Robotics and Autonomous Systems, 112: 72--83, 2019 (IF: 2.928), [Link]
Yunduan Cui, James Poon, Jaime Valls Miro, Kimitoshi Yamazaki, Kenji Sugimoto, and Takamitsu Matsubara. “Environment-adaptive interaction primitives through visual context for human–robot motor skill learning.” Autonomous Robots, 43(5), pp.1225-1240, 2019 (IF: 3.634), [Link]
James Poon, Yunduan Cui, Jaime Valls Miro, and Takamitsu Matsubara, “Learning from Demonstration for Locally Assistive Mobility Aids.” International Journal of Intelligent Robotics and Applications, 3(3), pp.255-268, 2019, [Link]
Lingwei Zhu, Yunduan Cui, Go Takami, Hiroaki Kanokogi and Takamitsu Matsubara, “Scalable Reinforcement Learning for Plant-wide Control of Vinyl Acetate Monomer Process.” Control Engineering Practice, 97, April 2020, 104331 (IF: 3.232), [Link]
Kazuki Shibata, Tatsuya Miyano, Tomohiko Jimbo, and Takamitsu Matsubara: Robust shape estimation with false-positive contact detection, Robotics and Autonomous Systems, 129, 103527, 2020 (accepted, IF: 2.928), [Link, arXiv]
Yunduan Cui, Jun'ichiro Ooga, Akihito Ogawa, and Takamitsu Matsubara: Probabilistic Active Filtering with Gaussian Processes for Occluded Object Search in Clutter, Applied Intelligence, 50(12), pp.4310-4324, 2020 (IF: 2.882), [Link]
Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata, Yukio Onuki and Takamitsu Matsubara: Bayesian Policy Optimization for Waste Crane with Garbage Inhomogeneity, IEEE Robotics and Automation Letters, 5(3), pp.4533-4540, 2020 (with CASE 2020 option), [Link]
Tatsuya Teramae, Takamitsu Matsubara, Tomoyuki Noda, Jun Morimoto: Quaternion-based Trajectory Optimization of Human Postures for Inducing Target Muscle Activation Patterns, IEEE Robotics and Automation Letters, 5(4), pp.6607-6614, 2020 (with IROS 2020 option), [Link]
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel Georgina Ramirez-Alpizar, Takayuki Nishi, Shinichi Kikuchi, Takamitsu Matsubara, Kensuke Harada: Learning Force Control for Contact-Rich Manipulation Tasks With Rigid Position-Controlled Robots, IEEE Robotics and Automation Letters, 5(4), pp.5709-5716, 2020 (with IROS 2020 option), [Link, arXiv]
Tomoya Miyamoto, Hikaru Sasaki, Takamitsu Matsubara: Exploiting Visual-outer Shape for Tactile-inner Shape Estimation of Objects Covered with Soft Materials, IEEE Robotics Automation Letters, 5(4), pp.6278-6285, 2020 (with IROS 2020 option), [Link]
Yunduan Cui, Shigeki Osaki, Takamitsu Matsubara: Autonomous Boat Driving System using Sample-efficient Model Predictive Control-based Reinforcement Learning Approach, Journal of Field Robotics, 38(3), pp.331-354, 2021 (IF: 3.581), [Link]
Masashi Hamaya, Takamitsu Matsubara, Tatsuya Teramae, Tomoyuki Noda and Jun Morimoto; Design of Physical User–robot Interactions for Model Identification of Soft Actuators on Exoskeleton Robots, International Journal of Robotics Research, 40(1), pp.397-410, 2021 (IF: 6.134), [Link]
Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui, Tamim Asfour, Takamitsu Matsubara: Uncertainty-aware Contact-safe Model-based Reinforcement Learning, IEEE Robotics and Automation Letters, 6(2), pp.3918-3925, 2021 (with ICRA 2021 option), [IEEE, arXiv]
Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara: Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester, Journal of Intelligent Robotics and Applications, 6, pp.10-22, 2021, [Springer, arXiv]
Keiji Nagatani, Masato Abe, Koichi Osuka, Pang-jo Chun, Takayuki Okatani, Mayuko Nishio, Shota Chikushi, Takamitsu Matsubara, Yusuke Ikemoto, Hajime Asama: Innovative Technologies for Infrastructure Construction and Maintenance Through Collaborative Robots based on an Open Design Approach, Advanced Robotics, 35(11), pp.715-722, 2021, [Link]
Naoto Komeno and Takamitsu Matsubara: Tactile Perception based on Injected Vibration in Soft Sensor, IEEE Robotics Autom. Lett. 6(3), 5365-5372 (2021) (with Humanoids 2020 option), [IEEE, arXiv]
Hikaru Sasaki and Takamitsu Matsubara: Variational Policy Search using Sparse Gaussian Process Priors for Learning Multimodal Optimal Actions, Neural Networks, 143, pp.291-302, 2021, [Elsevier, arXiv]
Yuki Kadokawa, Yoshihisa Tsurumine and Takamitsu Matsubara: Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA , IEEE Robotics and Automation Letters, 6(4), pp. 8545-8552 (2021), [IEEE, arXiv]
Yujun Lai, Gavin Paul, Yunduan Cui, Takamitsu Matsubara : User Intent Estimation during Robot Learning using Physical Human-Robot Interaction Primitives, Autonomous Robots, 46, pp.421-436, 2022, [Springer]
Brendan Michael, Akifumi Ise, Kaoru Kawabata, Takamitsu Matsubara: Task-Relevant Encoding of Domain Knowledge in Dynamics Modelling: Application to Furnace Forecasting from Video, IEEE Access,10, pp.4615-4627, 2022, [IEEE]
Lingwei Zhu, Go Takami, Mizuo Kawahara, Hiroaki Kanokogi, Takamitsu Matsubara: Alleviating Parameter-tuning Burden in Reinforcement Learning for Large-scale Process Control, Computers and Chemical Engneering, 158, 107658, 2022, [Elsevier]
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, and Takamitsu Matsubara: Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation, IEEE Robotics and Automation Letters, 7(4), 8964 - 8971 (2022) (with IROS2022 option), [IEEE, HP, arXiv]
Yoshihisa Tsurumine and Takamitsu Matsubara: Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation, Robotics and Autonomous Systems, 158, 104264 , 2022 (IF: 3.7), [Elsevier, arXiv]
Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura, Sadao Kawamura, and Takamitsu Matsubara: Physically Consistent Preferential Bayesian Optimization for Food Arrangement, IEEE Robotics and Automation Letters, 7(4), pp. 11863-11870 (2022), [IEEE, HP, arXiv]
Felix von Drigalski, Kazumi Kasaura, Cristian C. Beltran-Hernandez, Masashi Hamaya, Kazutoshi Tanaka, and Takamitsu Matsubara: Uncertainty-aware Manipulation Planning using Gravity and Environment Geometry, IEEE Robotics Automation Letters, 7(4), pp.11942-11949, 2022, [IEEE]
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara: Deep Reinforcement Learning of Event-triggered Communication and Consensus-based Control for Distributed Cooperative Transport, Robotics and Autonomous Systems, 159, 104307, 2023, [Link]
Hanbit Oh, Hikaru Sasaki, Brendan Michael, Takamitsu Matsubara: Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation, Neural Networks, 158, pp.42-58, 2023, [Link]
Yaqiang Mo, Hikaru Sasaki, Takamitsu Matsubara, Kimitoshi Yamazaki: Multi-step Motion Learning by Combining Learning-from-Demonstration and Policy-Search, Advanced Robotics, 37(9), pp.560-575, 2023, [Link]
Araki Wakiuchi, Shogo Takasuka, Shigehito Asano, Ryo Hashizume, Aniruddha Nag, Miho Hatanaka, Tomoyuki Miyao, Yuya Ohnishi, Takamitsu Matsubara, Tsuyoshi Ando, Tetsunori Sugawara, Mikiya Fujii, Hiroharu Ajiro: Composition Regulation by Flow Copolymerization of Methyl Methacrylate and Glycidyl Methacrylate with Free Radical Method, Macromolecular Materials and Engineering, 2200626, 2023, [Link]
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Edgar Anarossi, Takamitsu Matsubara: Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks, IEEE Robotics and Automation Letters, 8(5), pp. 2724-2731 (2023), [IEEE, arXiv]
Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine, Takamitsu Matsubara: Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization, Robotics and Autonomous Systems, Vol. 165, 2023, 104425[Link, arXiv, YouTube]
Shogo Takasuka, Shunto Oikawa, Takayoshi Yoshimura, Sho Ito, Yosuke Harashima, Tomoaki Takayama, Shigehito Asano, Akira Kurosawa, Tetsunori Sugawara, Miho Hatanaka, Tomoyuki Miyao, Takamitsu Matsubara, Yu-ya Ohnishi, Hiroharu Ajiro, Mikiya Fujii: Extrapolation performance improvement by quantum chemical calculations for machine-learning-based predictions of flow-synthesized binary copolymers, Digital Discovery, 2023, [Link]
Araki Wakiuchi, Swarit Jasial, Shigehito Asano, Ryo Hashizume, Miho Hatanaka, Yu-ya Ohnishi, Takamitsu Matsubara, Hiroharu Ajiro, Tetsunori Sugawara, Mikiya Fujii, Tomoyuki Miyao: Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra, ACS Omega 2023, [Link]
Tatsuya Sakuma, Takuya Kiyokawa, Takamitsu Matsubara, Jun Takamatsu, Takahiro Wada, and Tsukasa Ogasawara: Jamming Gripper-Inspired Soft Jig for Perceptive Parts Fixing, IEEE Access, vol. 11, pp.62187-62199, 2023, [Link, YouTube]
Lingwei Zhu and Takamitsu Matsubara: Cautious Policy Programming: Exploiting KL Regularization for Monotonic Policy Improvement in Reinforcement Learning, Machine Learning Journal, 2023, [Link, arXiv]
Cheng-Yu Kuo, Hirofumi Shin, and Takamitsu Matsubara: Reinforcement Learning with Energy-exchange Dynamics for Spring-loaded Biped Robot Walking, IEEE Robotics and Automation Letters, 8(10), pp.6243-6250, 2023, [Link]
Takumi Hachimine, Jun Morimoto, and Takamitsu Matsubara: Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning, IEEE Robotics and Automation Letters, 8(10), pp. 6325-6242, 2023, [IEEE, HP, arXiv, YouTube]
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, and Takamitsu Matsubara: AdaTerm: Adaptive T-Distribution Estimated Robust Moments towards Noise-Robust Stochastic Gradient Optimizer, Neurocomputing, vol. 557, p. 126692, 2023, [Link, arXiv]
Wataru Hatanaka, Ryota Yamashina, and Takamitsu Matsubara: Reinforcement Learning of Action and Query Policies with LTL Instructions under Uncertain Event Detector, IEEE Robotics and Automation Letters, 8(11), pp.7010-7017, 2023, [IEEE, arXiv]
Yuhwan Kwon, Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata, and Takamitsu Matsubara: Policy Optimization for Waste Crane Automation from Human Preferences, IEEE Access, vol. 11, pp. 126524-126541, 2023, [Link]
Naoto Komeno and Takamitsu Matsubara: Incipient Slip Detection by Vibration Injection into Soft Sensor, IEEE Robotics and Automation Letters, 9(4), pp.3251-3258, 2024, [IEEE, arXiv, YouTube]
Hanbit Oh and Takamitsu Matsubara: Leveraging Demonstrator-Perceived Precision for Safe Interactive Imitation Learning of Clearance-Limited Tasks, IEEE Robotics and Automation Letters, 9(4), pp.3387-3394, 2024, [IEEE, HP, arXiv, YouTube]
Takanori Jin, Taisuke Kobayashi, and Takamitsu Matsubara: Constrained Footstep Planning using Model-based Reinforcement Learning in Virtual Constraint-based Walking, Advanced Robotics, 38(8), pp.541-561, 2024, [Link]
Yuki Kadokawa, Tomohito Kodera, Yoshihisa Tsurumine, Shinya Nishimura, Takamitsu Matsubara: Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots, Robotics and Autonomous Systems, vol. 181, 104782, 2024, [Link, arXiv]
Cynthia Ochoa, Hanbit Oh, Yuhwan Kwon, Yukiyasu Domae, Takamitsu Matsubara: ISPIL: Interactive Sub-Goal-Planning Imitation Learning for Long-Horizon Tasks With Diverse Goals, IEEE Access, vol. 12, pp. 197616-197631, 2024 [IEEE]
Takumi Hachimine, Jun Morimoto, and Takamitsu Matsubara: Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding, IEEE Robotics and Automation Letters, 10(2), pp. 1162-1169, 2025 [HP, IEEE, arXiv]
Wataru Hatanaka, Ryota Yamashina, Takamitsu Matsubara: Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications, IEEE Robotics and Automation Letters, accepted [IEEE, arXiv]
Shu-yuan Wang, Hikaru Sasaki, Takamitsu Matsubara: Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies, Applied Intelligence, vol. 55, 436, 2025 [arXiv Springer ]
Tatsuya Teramae, Takamitsu Matsubara, Tomoyuki Noda, Jun Morimoto: Optimizing Non-assisted Body Part Movements for Robot-assisted Therapy, Biomedical Signal Processing and Control, Volume 107, September 2025, 107817 [Link]
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara: Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning, IJRR (Accepted)
Tatsuya Teramae, Takamitsu Matsubara, Tomoyuki Noda and Jun Morimoto: Neural-Enhanced Motion-to-EMG: Refining Simulated Muscle Activity from Musculoskeletal Models using a Seq2Seq Approach, Frontiers in Bioengineering and Biotechnology, Volume 13 - 2025 [Link]
Edgar Anarossi, Yuhwan Kwon, Hirotaka Tahara, Shohei Tanaka, Keisuke Shirai, Masashi Hamaya, Cristian C. Beltran Hernandez, Atsushi Hashimoto, and Takamitsu Matsubara: KeyMPs: One-Shot Vision-Language Guided Motion Generation by Sequencing DMPs for Occlusion-Rich Tasks, IEEE Access, vol. 13, pp. 125420-125441, 2025 [HP, IEEE, arXiv]
Yuki Kadokawa, Hirotaka Tahara, Takamitsu Matsubara: Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning, IEEE T-ASE, Vol. 22, page 18682 - 18693, 2025 [IEEE, arXiv, youtube]
Conference
Conference
James Poon, Yunduan Cui, Junichiro Ooga, Akihito Ogawa, and Takamitsu Matsubara: Probabilistic Active Filtering for Object Search in Clutter, IEEE 2019 International Conference on Robotics and Automation (ICRA 2019), pp.7256--7261, 2019, [Link]
Hikaru Sasaki and Takamitsu Matsubara: Multimodal Policy Search using Overlapping Mixtures of Sparse Gaussian Process Prior, IEEE International Conference on Robotics and Automation (ICRA 2019), pp.2433-2439, 2019, [Link]
Masashi Hamaya, Takamitsu Matsubara, Jun-ichiro Furukawa, Yuting Sun, Satoshi Yagi, Tatsuya Teramae, Tomoyuki Noda, and Jun Morimoto: Exploiting Human and Robot Muscle Synergies for Human-in-the-loop Optimization of EMG-based Assistive Strategies, IEEE 2019 International Conference on Robotics and Automation (ICRA 2019), pp.549--555, 2019, [Link]
Takumi Kaneko, Yoshihisa Tsurumine, James Poon, Yukio Onuki, Yingda Dai, Kaoru Kawabata, and Takamitsu Matsubara: Learning Deep Dynamical Models of a Waste Incineration Plant from In-furnace Images and Process Data, 15th IEEE International Conference on Automation Science and Engineering (CASE 2019), pp. 873-878, 2019, [Link]
Yoshihisa Tsurumine, Yunduan Cui, Kimitoshi Yamazaki, Takamitsu Matsubara: Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation, 2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2019), pp.290—296, [Link]
Yunduan Cui, Shigeki Osaki and Takamitsu Matsubara: Reinforcement Learning Boat Autopilot: A Sample-efficient and Model Predictive Control based Approach, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2019: 2868-2875, (IROS 2019), [Link]
Yuhwan Kwon, Takumi Kaneko, Yoshihisa Tsurumine, Hikaru Sasaki (NAIST), Kimiko Motonaka, Seiji Miyoshi (Kansai U) and Takamitsu Matsubara (NAIST): Combining Model Predictive Path Integral with Kalman Variational Auto-Encoder for Robot Control from Raw Images, IEEE/SICE International Symposium on System Integration (SII2020) , pp.271-276, [Link]
Lingwei Zhu, Yunduan Cui, Takamitsu Matsubara: Dynamic Actor-Advisor Programming for Scalable Safe Reinforcement Learning, IEEE 2020 International Conference on Robotics and Automation (ICRA 2020), pp. 10681-10687, [Link]
Cheng-Yu Kuo, Yunduan Cui, Takamitsu Matsubara: Sample-and-computational-efficient Probabilistic Model Predictive Control with Random Features, IEEE 2020 International Conference on Robotics and Automation (ICRA 2020), pp. 307-313, [Link]
Felix Wolf Hans Erich von Drigalski (OSX), Shohei Taniguchi (OSX), Robert Lee (OSX), Takamitsu Matsubara, Masashi Hamaya (OSX), Kazutoshi Tanaka (OSX), Yoshihisa Ijiri (OSX): Contact-based in-hand pose estimation using particle filtering, IEEE 2020 International Conference on Robotics and Automation (ICRA 2020), pp. 7294-7299, [Link]
Junki Matsuoka, Yoshihisa Tsurumine, Yuhwan Kwon, Takamitsu Matsubara, Takeshi Shimmura (Ganko Food Service), Sadao Kawamura (Ritsumeikan U): Learning Food-arrangement Policies from Raw Images with Generative Adversarial Imitation Learning, IEEE 17th International Conference on Ubiquitous Robots (UR2020), pp.93-98, 2020, [Link]
Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata, Yukio Onuki and Takamitsu Matsubara: Bayesian Policy Optimization for Waste Crane with Garbage Inhomogeneity, IEEE International Conference on Automation Science and Engineering (CASE 2020), 2020, (as presentation option of RA-L)
Tomoya Miyamoto, Hikaru Sasaki, Takamitsu Matsubara: Exploiting Visual-outer Shape for Tactile-inner Shape Estimation of Objects Covered with Soft Materials, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), 2020, (as presentation option of RA-L)
Tatsuya Teramae, Takamitsu Matsubara, Tomoyuki Noda, Jun Morimoto: Quaternion-based Trajectory Optimization of Human Postures for Inducing Target Muscle Activation Patterns, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020) (as presentation option of RA-L)
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel Georgina Ramirez-Alpizar, Takayuki Nishi, Shinichi Kikuchi, Takamitsu Matsubara, Kensuke Harada: Learning Contact-Rich Manipulation Tasks with Rigid Position-Controlled Robots: Learning to Force Control, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020) (as presentation option of RA-L)
Masashi Hamaya, Felix Wolf Hans Erich von Drigalski, Takamitsu Matsubara, Kazutoshi Tanaka, Robert Lee, Chisato Nakashima, Yoshiya Shibata, Yoshihisa Ijiri: Learning Soft Robotic Assembly Strategies from Successful and Failed Demonstration, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2020), 8309-8315
Hanbit Oh, Hikaru Sasaki, Brendan Michael, Takamitsu Matsubara: Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies, IEEE International Conference on Robotics and Automation (ICRA2021), 8629–8635, 2021
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara: Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport, IEEE International Conference on Robotics and Automation (ICRA2021), pp. 8671-8677, 2021
Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui, Tamim Asfour, Takamitsu Matsubara: Uncertainty-aware Contact-safe Model-based Reinforcement Learning, IEEE International Conference on Robotics and Automation (ICRA2021) (as presentation option of RA-L)
Naoto Komeno and Takamitsu Matsubara: Tactile Perception based on Injected Vibration in Soft Sensor, IEEE-RAS International Conference on Humanoid Robots (Humanoids2021) (as presentation option of RA-L)
Kazutoshi Tanaka, Masashi Hamaya, Devwrat Joshi, Felix von Drigalski, Ryo Yonetani, Takamitsu Matsubara, and Yoshihisa Ijiri: Learning Robotic Contact Juggling, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 958-964, 2021
Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara: Cautious Actor-Critic, The 13th Asian Conference on Machine Learning (ACML2021), pp. 220-235 [30.4%, 115/378]
Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara: Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning, The 13th Asian Conference on Machine Learning (ACML2021), pp. 918-931 [30.4%, 115/378]
Yoshihisa Tsurumine, Takamitsu Matsubara: Variationally Autoencoded Dynamic Policy Programming for Robotic Cloth Manipulation Planning based on Raw Images, IEEE/SICE International Symposium on System Integration (SII2022), pp.416-421
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Brendan Michael, Takamitsu Matsubara: Disturbance-Injected Robust Imitation Learning with Task Achievement, IEEE International Conference on Robotics and Automation (ICRA2022), pp.2466-2472, 2022 [arXiv, YouTube]
Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata, Takamitsu Matsubara: Gaussian Process Self-triggered Policy Search in Weakly Observable Environments , IEEE International Conference on Robotics and Automation (ICRA2022), pp.5946-5952, 2022 [arXiv, YouTube]
Naoto Komeno, Brendan Michael, Katharina Küchler, Edgar Anarossi, Takamitsu Matsubara: Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics, SICE Annual Conference 2022 (SICE2022), pp.333-340 [arXiv, IEEE, YouTube]
Atsuki Nagata, Takamitsu Matsubara and Kenji Sugimoto: Disturbance Suppression in Feedback Error Learning Control, SICE Annual Conference 2022 (SICE2022), pp.944-949
Tatsuya Shimizu, Taisuke Kobayashi and Takamitsu Matsubara: Study on Hierarchical Reinforcement Learning for Demand Response Product Rollout, SICE Annual Conference 2022 (SICE2022), pp.114-117
Taichi Taniguchi, Hikaru Sasaki, Takamitsu Matsubara: In-bucket Garbage Shape Estimation using Time-varying Gaussian Process Implicit Surface with Proximity Sensor, The 54th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS ‘22), 2022
Kakeru Fujikura, Kenta Hanada, Kenji Sugimoto, Takamitsu Matsubara: Sliding Innovation Filter for Linear State Equation of Underwater Drones with Modeling Errors, The 54th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS ‘22), 2022
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, and Takamitsu Matsubara: Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2022) (as presentation option of RA-L)
Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura, Sadao Kawamura, and Takamitsu Matsubara: Physically Consistent Preferential Bayesian Optimization for Food Arrangement, IEEE International Conference on Robotics and Automation (ICRA2023) (as presentation option of RA-L)
Yuki Kadokawa, Yoshihisa Tsurumine and Takamitsu Matsubara: Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA, IEEE International Conference on Robotics and Automation (ICRA2023) (as presentation option of RA-L)
Hikaru Sasaki, Go Watanabe, Terushi Hirabayashi, Kaoru Kawabata, Takamitsu Matsubara: Learning Re-grabbing Policies based on Grabbed Garbage Weight Estimation using In-bucket Images for Waste Cranes, IFAC World Congress, pp.5494-5499, 2023
Kazuki Shibata, Tomohiko Jimbo, Tadashi Odashima, Keisuke Takeshita, Takamitsu Matsubara: Learning Locally, Communicating Globally: Reinforcement Learning of Multi-Robot Task Allocation for Cooperative Transport, IFAC World Congress, pp.12278-12285, 2023
Edgar Anarossi, Hirotaka Tahara, Naoto Komeno, Takamitsu Matsubara: Deep Segmented DMP Networks for Learning Discontinuous Motions: IEEE International Conference on Automation Science and Engineering (CASE), pp.1-7, 2023 [IEEE, arXiv, YouTube]
Shu-yuan Wang, Hikaru Sasaki, Takamitsu Matsubara: Non-Gaussian Overlapping Mixtures of Gaussian Processes for Learning Multimodal Robot Policies, SICE Annual Conference 2023 (SICE2023), pp. 545-549, 2023
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, and Takamitsu Matsubara: Domains as Objectives: Multi-Domain Reinforcement Learning with Convex-Coverage Set Learning for Domain Uncertainty Awareness, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 [IEEE]
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Edgar Anarossi, and Takamitsu Matsubara: Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 (as presentation option of RA-L)
Wataru Hatanaka, Ryota Yamashina, and Takamitsu Matsubara: Reinforcement Learning of Action and Query Policies with LTL Instructions under Uncertain Event Detector, IEEE International Conference on Robotics and Automation (ICRA), 2024 (as presentation option of RA-L)
Takumi Hachimine, Jun Morimoto, and Takamitsu Matsubara: Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning, IEEE International Conference on Robotics and Automation (ICRA), 2024 (as presentation option of RA-L)
Cheng-Yu Kuo, Hirofumi Shin, and Takamitsu Matsubara: Reinforcement Learning with Energy-exchange Dynamics for Spring-loaded Biped Robot Walking, IEEE International Conference on Robotics and Automation (ICRA), 2024 (as presentation option of RA-L)
Takanori Jin, Taisuke Kobayashi, and Takamitsu Matsubara: Walking in Constrained Environment Using Model-Based Reinforcement Learning for Virtual Constraint-Based Gait, IEEE International Conference on Robotics and Automation (ICRA), 2024
Hanbit Oh and Takamitsu Matsubara: Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 (as presentation option of RA-L)
Naoto Komeno and Takamitsu Matsubara: Incipient Slip Detection by Vibration Injection into Soft Sensor, IEEE/RSJInternational Conference on Intelligent Robots and Systems (IROS), 2024 (as presentation option of RA-L)
Jia Qu, Shun Otsubo, Tomoya Yamanokuchi, Takamitsu Matsubara and Shotaro Miwa: Domain Randomization-free Sim-to-Real: An Attention-Augmented Memory Approach for Robotic Tasks, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
David Gutierrez-Moreno (TUM), Simon Armleder (TUM), Yuhwan Kwon, Takumi Hachimine, Yoshihisa Tsurumine, Takamitsu Matsubara and Gordon Cheng (TUM): Diffusion Model and RRT*-Based Methods for Reflected Mass Optimization in Motion Planning, IEEE/SICE International Symposium on System Integration (SII), 2025
Yusei Naito (Toyota), Tomohiko Jimbo (Toyota), Tadashi Odashima (Toyota), Takamitsu Matsubara: Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport, IEEE/SICE International Symposium on System Integration (SII), 2025
Yuma Shida (Toyota), Tomohiko Jimbo (Toyota), Tadashi Odashima (Toyota), Takamitsu Matsubara: Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks, IEEE/SICE International Symposium on System Integration (SII), 2025
Takuya Kiyokawa, Eiki Nagata, Yoshihisa Tsurumine, Yuhwan Kwon, Takamitsu Matsubara: Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move, IEEE/SICE International Symposium on System Integration (SII), 2025
Ing-Sheng Bernard-Tiong, Yoshihisa Tsurumine, Ryosuke Sota, Kazuki Shibata, Takamitsu Matsubara: Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation, IEEE/SICE International Symposium on System Integration (SII), 2025
Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara: Feasibility-aware Imitation Learning from Observation through a Hand-mounted Demonstration Interface, IEEE International Conference on Robotics and Automation (ICRA), 2025, Accepted [arXiv]
Ryo Watanabe, Takahiro Miki, Fan Shi, Yuki Kadokawa, Filip Bjelonic, Kento Kawaharazuka, Andrei Cramariuc, Marco Hutter: Learning Quiet Walking for a Small Home Robot, IEEE International Conference on Robotics and Automation (ICRA), 2025, Accepted [arXiv]
Wataru Hatanaka, Ryota Yamashina, Takamitsu Matsubara: Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications, IROS2025 (Accepted as RA-L option) [IEEE, arXiv]
Takumi Hachimine, Jun Morimoto, and Takamitsu Matsubara: Cutting Sequence Diffuser: Sim-to-Real Transferable Planning for Object Shaping by Grinding, IROS2025 (Accepted as RA-L option) [HP, IEEE, arXiv]
Yoshiki Yano, Kazuki Shibata, Maarten Kokshoorn, Takamitsu Matsubara: ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control, IROS 2025 (Accepted) [HP, arXiv, youtube]
Tomoya Yamanokuchi, Alberto Bacchin, Emilio Olivastri, Takamitsu Matsubara, Emanuele Menegatti: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning, The 19th international conference on Intelligent Autonomous Systems (IAS) [HP, arXiv, youtube]
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