Journal papers

  • Furukawa,J., Morimoto,J.(2021/01).
    Composing an assistive control strategy based on linear bellman combination from estimated user’s motor goal
    IEEE Robotics and Automation Letters Vol.6,No.2,pp.1051-1058
    https://doi.org/10.1109/LRA.2021.3051562
  • Teramae,T., Matsubara,T., Noda,T., Morimoto,J.(2020/10).
    Quaternion-based trajectory optimization of human postures for inducing target
    IEEE Robotics and Automation Letters Vol.5, No.4, pp.6607-6614
    https://doi.org/10.1109/LRA.2020.3015460
  • Uchibe,E., Doya,K.(2020/08).
    Imitation learning based on entropy-regularized forward and inverse reinforcement learning
    arXiv(Web)
    https://arxiv.org/abs/2008.07284
  • Pahic,R., Ridge,B., Gams,A., Morimoto,J., Ude,A.(2020/04).
    Training of deep neural networks for the generation of dynamic movement primitives
    Neural Netowrks Vol.127, pp.121-131
    https://doi.org/10.1016/j.neunet.2020.04.010
  • Ohnishi,S., Uchibe,E., Yamaguchi,Y., Nakanishi,K., Yasui,Y., Ishii,S. (2019/12).
    Constrained deep Q-learning gradually approaching ordinary Q-learning
    Frontiers in Neurorobotics Vol.13, Article 103
    https://doi.org/10.3389/fnbot.2019.00103
  • Ishihara,K., Itoh D.T., Morimoto,J. (2019/10).
    Full-body optimal control toward versatile and agile behaviors in a humanoid robot
    “IEEE Robotics and Automation Letters Vol.5,No.1,pp.119-126”
    https://doi.org/10.1109/LRA.2019.2947001
  • Iwane,F., Lisi,G., Morimoti,J. (2019/08).
    EEG sensorimotor correlates of speed during forearm passive movements
    IEEE Transactions on Neural Systems and Rehabilitation Engineering Vol.27,Issue9, pp.1667-1675
    https://doi.org/10.1109/TNSRE.2019.2934231
  • Hamaya,M., Matsubara,T., Teramae,T., Noda,T., Morimoto,J. (2019/06).
    Design of physical user-robot interactions for model identification of soft
    International Journal of Robotics Research
    https://doi.org/10.1177/0278364919853618
  • Ugurlu B., Forni,P., Doppmann,C., Sariyiliz,E., Morimoto,J. (2019/05).
    Stable control of force, position, and stiffness for robot joints powered via pneumatic muscles
    IEEE Transactions on Industrial Informatics
    https://doi.org/10.1109/TII.2019.2916228
  • Petric,T., Peternel,L., Morimoto,J., Babic,J. (2019/05).
    Assistive arm-exoskeleton control based on human muscular manipulability
    Frontiers in Neurorobotics Vol.13, Article 30
    https://doi.org/10.3389/fnbot.2019.00030
  • Tsurumine, Y.,Cui,Y., Uchibe,E., Matsubara,T. (2018/11).
    Deep reinforcement learning with smooth policy update: application to robotic cloth manipulation
    Robotics and Autonomous Systems Vol.112, pp.72-83
    https://doi.org/10.1016/j.robot.2018.11.004
  • Teramae,T., Ishihara, K., Babic,J., Morimoto,J. Oztop,E. (2018/11).
    Human-in-the-loop control and task learning for pneumatically actuated muscle based robots
    Frontiers in Human-in-the-Loop Robot Control and Learning　　　　　　　　　　　　　　　　　　Vol.12, Article 71
    https://doi.org/10.3389/fnbot.2018.00071
  • Uchibe,E. (2018/09).
    Cooperative and competitive reinforcement and imitation learning for a mixture of heterogeneous learning modules
    Frontiers in Neurorobotics Vol.12,Artcle61
    https://doi.org/10.3389/fnbot.2018.00061
  • Ewerton,M., Rother, D., Weimar, O.J., Kollegger,G., Wiemeyer J., Peters, J., Maeda,G (2018/05).
    Assisting movement training and execution with visual and haptic feedback
    Frontiers in Robotics and AI Vol.24, Article 24, pp.1-19
    https://doi.org/10.3389/fnbot.2018.00024
  • Lisi,G., Rivela,D., Takai,A., Morimoto,J. (2018/02).
    Markov switching model for quick detection of event related desynchronization in EEG
    Frontiers in Neuroscience-Neuroprosthetics Vol.12, Article 24
    https://doi.org/10.3389/fnins.2018.00024
  • Ishihara,K., Morimoto,J. (2018/01).
    An optimal control strategy for hybrid actuator systems: application to an artificial muscle with electric motor assist
    Neural Networks Vol.99, pp.92-100
    https://doi.org/10.1016/j.neunet.2017.12.010
  • Gasper,T., Nemec,B., Morimoto,J., Ude,A. (2017/12).
    Skill learning and action recognition by arc-length dynamic movement primitives
    Robotics and Autonomous Systems Vol.100, pp.225-235
    https://doi.org/10.1016/j.robot.2017.11.012
  • Hamaya,M., Tatsubara,T., Noda,T., Teramae,T., Morimoto,J. (2017/11).
    Learning assistive strategies for exoskeleton robots from user-robot physical interaction
    Pattern Recognition Letters Vol.99, pp.67-76
    https://doi.org/10.1016/j.patrec.2017.04.007
  • Kozono,T., Uchibe,E., Doya,K. (2017/10).
    Unifying value iteration, advantage learning, and dynamic policy programming
    arXiv.org(Web) arXiv:1710.10866
    https://arxiv.org/abs/1710.10866
  • Kinjo,K., Uchibe,E., Doya,K. (2017/10).
    Robustness of linearly solvable markov games employing inaccurate dynamics model
    Journal of Artificial Life and Robotics Vol.23,Issue1, pp.1-9
    https://doi.org/10.1007/s10015-017-0401-2
  • Teramae,T., Noda,T., Morimoto,J. (2017/08).
    EMG-based model predictive control for physical human-robot interaction: Application for assist-as-needed control
    IEEE Robotics and Automation Letters(RA-L) Vol.3, No.1, pp.210-217
    https://doi.org/10.1109/LRA.2017.2737478
  • Ichikawa,N., Lisi,G., Yahata,N., Okada,G., Takamura,M., Yamada,M., Suhara,T., Hashimoto,R., Yamada,T., Yoshihara,Y., Takahashi,H., Kasai,K., Kato,N., Yamawaki,S., Kawato,M., Morimoto,J., Okamoto,Y. (2017/04).
    Identifying melancholic depression biomarker using whole-brain functional connectivity
    arXiv.org(Web) arXiv:1704.01039
    https://arxiv.org/abs/1704.01039
  • Furukawa,J., Noda,T., Teramae,T., Morimoto,J. (2017/04).
    Human movement modeling to detect biosignal sensor failures for myoelectric assistive robot control
    IEEE Transactions on Robotics Vol.33, No.4, pp.846-856
    https://ieeexplore.ieee.org/document/7906627
  • Wang,J., Uchibe,E., Doya,K. (2017/01).
    Adaptive baseline enhances EM-based policy search: validation in a view-based positioning task of a smartphone balancer
    Frontiers in Neurorobotics(Web) Vol.11, Article 1
    https://doi.org/10.3389/fnbot.2017.00001
  • Elfwing,S., Uchibe,E., Doya,K. (2016/08).
    From free energy to expected energy:improving energy-based value function approximation in reinforcement learning
    Neural Networks Vol.84,pp.17-27
    https://doi.org/10.1016/j.neunet.2016.07.013
  • 内部英治 (2016/03).
    線形可解マルコフ決定過程を用いた順・逆強化学習
    日本神経回路学会誌 Vol.23, No.1, pp.2-13
    https://doi.org/10.3902/jnns.23.2
  • Sugimoto,N., Tangkaratta,V., Wensveen,T., Zhao,T., Sugiyama,M., Morimoto,J. (2016/02).
    Trial and error: using previous experiences as simulation models in humanoid motor learning
    IEEE Robotics and Automation Magazine Vol.23, Issue 1, pp.96-105
    https://doi.org/10.1109/MRA.2015.2511681
  • Peternel,L., Noda,T., Petric,T., Ude,A., Morimoto,J., Babic,J. (2016/02).
    Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation
    PLoS ONE Vol.11, Issue2, e0148942
    https://doi.org/10.1371/journal.pone.0148942
  • Gams,A., Petric,T., Do,M., Nemec,B., Morimoto,J., Asfour,T., Ude,A. (2016/01).
    Adaptation and coaching of periodic motion primitives through physical and visual interaction
    Robotics and Autonomous Systems Vol.75, Part B, pp.340-351
    https://doi.org/10.1016/j.robot.2015.09.011
  • Ugurlu,B., Doppmann,C., Hamaya,M., Forni,P., Teramae,T., Noda,T., Morimoto,J. (2015/06).
    Variable ankle stiffness improves balance control:experiments on a bipedal exoskeleton
    IEEE Transactions on Mechatronics Vol.21,No.1,pp.79-87
    https://doi.org/10.1109/TMECH.2015.2448932
  • Tangkaratt, V., Mori, S., Zhao, T., Mmorimoto, J., Sugiyama, M. (2015).
    Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation
    Neural Networks, Vol.57, 128-140
  • Morimoto, J., Kawato, M. (2015).
    Creating the brain and interacting with the brain:an Integrated approach to understanding the brain
    Journal of the Royal Society Interface, Vol.12, 104, 20141250
  • Furukawa, J., Noda, T., Teramae, T., Morimoto, J. (2015).
    Fault tolerant approach for biosignal-based robot control
    Advanced Robotics, Vol.29, 7, 505-514.
  • Lisi, G., Morimoto, J. (2015).
    EEG single-trial detection of gait speed changes treadmill walk
    PLoS ONE, Vol.10, 4, 1-28.
  • Bouyarmane, K., Vaillant, J., Sugimoto, N., Keith, F., Furukawa, J., Morimoto, J. (2014).
    Brain-machine interfacing control of whole-body humanoid motion
    Frontiers in Systems Neurosciences, Vol.8, 138, 1-10.
  • Lisi, G., Noda, T., Morimoto, J. (2014).
    Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot
    Frontiers in Neuroscience, Vol.8, 85, 1-12.
  • Furukawa, J., Noda, T., Teramae, T., Morimoto, J. (2014).
    An EMG-driven weight support system with pneumatic artificial muscles
    IEEE Systems Journal, DOI:10.1109/JSYST.2014.2330376.
  • Ugurlu, B., Saglia, J., Tsagarakis, N., Morfey, S., Caldwell, D. (2014).
    Bipedal hopping pattern generation for passively compliant humanoids:exploiting the resonance
    IEEE Transactions on Industrial Electronics, Vol.61, 10, 5431-5443.
  • 森本淳, 杉本徳和 (2013).
    高次元・実環境における強化学習
    計測と制御, Vol.52, 8, 742-748.
  • Zhao, T., Hachiya, H., Tangkaratt, V., Morimoto, J., Sugiyama, M. (2013).
    Efficient sample reuse in policy gradients with parameter-based exploration
    Neural Computation, Vol.25, 6, 1512-1547.
  • Ariki, Y., Hyon, S., Morimoto, J. (2013).
    Extraction of primitive representation from captured human movements and measured ground reaction force to generate physically consistent imitated behaviors
    Neural Networks, Vol.40, 32-43.
  • Matsubara, T., Morimoto, J. (2013).
    Bilinear modeling of EMG Signals to extract user-independent features for multiuser myoelectric interface
    IEEE Transactions on Biomedical Engineering, Vol.60, 8, 2205-2213.
  • 松原崇充, 森本淳 (2013).
    多重時系列データ解析のための正準多重整列法
    電子情報通信学会論文誌D, Vol.J96-D, 2, 298-305.
  • Schiebener, D., Morimoto, J., Asfour, T., Ude, A. (2013).
    Integrating visual perception and manipulation for autonomous learning of object representations
    Adaptive Behavior, Vol.21, 5, 328-345.
  • Manoonpong, P., Kolodziejski, C., Worgotter, F., Morimoto, J. (2013).
    Combining correlation-based and reward-based learning in neural control for policy improvement
    Advances in Complex Systems, Vol.16, 2&3, 1350015-pp.1-38.
  • Forte, D., Gams, A., Morimoto, J., Ude, A. (2012).
    On-line motion synthesis and adaptation using a trajectory database
    Robotics and Autonomous Systems, Vol.60, 10, 1327-1339.
  • 内方章雅, 松原崇充, 森本淳 (2012).
    スタイル-位相適応に基づく周期運動の時空間同期:2足歩行運動への適用
    電子情報通信学会和文論文誌D, J95-D, 7, 1476-1487.
  • Sugimoto, N., Haruno, M., Doya, K., Kawato, M. (2012).
    Mosaic for multiple-reward environments.
    Neural Computation, 24, 3, 577-606.
  • Matsubara, T., Hyon, S., Morimoto, J. (2012).
    Real-time stylistic prediction for whole-body human motions.
    Neural Networks, 25, 191-199.
  • Matsubara, T., Hyon, S., Morimoto, J. (2011).
    Learning parametric dynamic movement primitives from multiple demonstrations.
    Neural Networks, 24, Issue 5, 493-500.
  • 松原崇充, 玄相昊, 森本淳 (2011).
    個性を考慮した周期的全身運動の予測.
    電子情報通信学会論文誌, J94-D, 1, 344-355.
  • Ude, A., Gams, A., Asfour, T., Morimoto, J. (2010).
    Task-specific generalization of discrete and periodic dynamic movement primitives.
    IEEE Transactions on Robotics, 26, 5, 800-815.
  • 玄相昊, 里宇明元 (2010).
    可変重力環境における全身運動制御と等身大ヒト型ロボットを用いた検証.
    バイオメカニズム学会誌, 34, 1, 5-11.
  • 玄相昊 (2009).
    準静的に獲得した関節軌道を利用して動的な類似運動を逐次的に学習する方法.
    日本ロボット学会誌, 27, 9, 1025-1028.
  • Hyon, S. (2009).
    A motor control strategy with virtual musculoskeletal systems for compliant anthropomorphic robots.
    IEEE/ASME Transactions on Mechatronics, 14, 6, 677-688.
  • Morimoto, J., Atkeson, C. G. (2009).
    Nonparametric representation of an approximated poincare map for learning biped locomotion.
    Autonomous Robots, Vol.27, 2, 131-144.
  • 玄相昊 (2009).
    複数の接地部分と冗長関節を有するヒューマノイドロボットの受動性に基づく最適接触力制御.
    日本ロボット学会誌, 27, 2, 178-187.
  • Hyon, S. (2009).
    Compliant terrain adaptation for biped humanoids without measuring ground surface and contact forces.
    IEEE Transactions on Robotics, 25, 1, 171-178.
  • Ude, A., Omrcen, D., Cheng, G. (2008).
    Making object learning and recognition an active process.
    International Journal of Humanoid Robotics, 5, 2, 267-286.
  • Hale, J. G., Hohl, B., Hyon, S., Matsubara, T., Moraud, E. M., Cheng, G. (2008).
    Highly precise dynamic simulation environment for humanoid robots.
    Advanced Robotics: Special Issue on Humanoid Technologies and Systems, 22, 10, 1075-1105.
  • Matsubara, T., Morimoto, J., Nakanishi, J., Hyon, S., Hale, J. G., Cheng, G. (2008).
    Learning to acquire whole-body humanoid center of mass movements to achieve dynamic tasks.
    Advanced Robotics: Special Issue on Humanoid Technologies and Systems, 22, 10, 1125-1142.
  • Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S. (2008).
    Operational Space Control: A theoretical and empirical comparison.
    International Journal of Robotics Research, 27, 6, 737-757.
  • 玄相昊, 藤本健治 (2008).
    ハミルトン力学系の対称軌道族と２足歩行の大域的歩容生成への応用
    日本ロボット学会誌, 26, 4, 372-380.
  • Moren, J., Ude, A., Koene, A., Cheng, G. (2008).
    Biologically based top-down attention modulation for humanoid interactions.
    International Journal of Humanoid Robotics, 5, 1, 3-24.
  • Endo, G., Morimoto, J., Matsubara, T., Nakanishi, J., Cheng, G. (2008).
    Learning CPG-based biped locomotion with a policy gradient method: Application to a humanoid robot.
    The International Journal of Robotics Research, Special Issue on Machine Learning in Robotics, 27, 2, 213-228.
  • Chaminade, T., Oztop, E., Cheng, G., Kawato, M. (2008).
    From self-observation to imitation: Visuomotor association on a robotic hand.
    Brain Research Bulletin, 75, 775-784.
  • Morimoto, J., Endo, G., Nakanishi, J., Cheng, G. (2008).
    A biologically inspired biped locomotion strategy for humanoid robots: Modulation of simple sinusoidal patterns by a coupled oscillator model.
    IEEE Transaction on Robotics, 24, 1, 185-191.
  • Cheng, G., Metta, G., Cannata, G., Sandini, G. (2008).
    Humanoid technologies:”Know-how”.
    Robotics and Autonomous Systems, 56, Issue 1, 1-3.
  • 佐藤訓志, 藤本健治, 玄相昊 (2007).
    ハミルトン系の変分対称性に基づく1脚ロボットの最適歩容生成
    計測自動制御学会論文集, 3, 12, 1103-1110.
  • Peters, J., Mistry, M., Udwadia, F., Nakanishi, J., Schaal, S. (2007).
    A unifying framework for robot control with redundant DOFs.
    Autonomous Robots, Vil.24, 1, 1-12.
  • Hyon, S., Hale, J. G., Cheng, G. (2007).
    Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces.
    IEEE Transactions on Robotics, 23, 5, 884-898.
  • Koene, A., Arnold, D., Johnston, A. (2007).
    Bimodal sensory discrimination is finer than dual single modality discrimination.
    Journal of Vision, 7, 11, Article14, 1-11.
  • Cheng, G., Hyon, S., Morimoto, J., Ude, A., Hale, J. G., Colvin, G., Scroggin, W., Jacobsen, S. C. (2007).
    CB: A humanoid research platform for exploring neuroscience.
    Journal of Advance Robotics, 21, 10, 1097-1114.
  • Ude, A., Moren, J., Cheng, G. (2007).
    Visual attention and distributed processing of visual information for the control of humanoid robots.
    Humanoid Robots Human-like Machines (International Journal of Advanced Robotic Systems), 423-436.
  • Morimoto, J., Atkeson, C. (2007).
    Learning biped locomotion: Application of poincare-map-based reinforcement learning.
    IEEE Robotics and Automation Magazine, 14, 2, 41-51.
  • Koene, A. R., Zhaoping, L. (2007).
    Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom-up saliency map in V1.
    Journal of Vision, 7, 7, Article 6, 1-14.
  • Morimoto, J., Doya, K. (2007).
    Reinforcement learning state estimator.
    Neural Computation, 19, 3, 730-756.
  • Oztop, E. (2006).
    An upper bound on the minimum number of onomials required to separate dichotomies of {-1,1}n.
    Neural Computation, 18, 3119-3138.
  • Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K. (2006).
    Learning CPG-based biped locomotion with a policy gradient method.
    Robotics and Autonomous Systems, 54,911-920.
  • 中西淳, Schaal, S. (2006).
    平成17年度日本神経回路学会論文賞受賞対象論文概要
    神経回路学会誌 2006年3月号 受賞概要, 13, 1, 37-38.
  • Oztop, E., Imamizu, H., Cheng, G., Kawato, M. (2006).
    A computational model of anterior intraparietal (AIP) neurons.
    Neurocomputing, 69, 1354-1361.
  • Oztop, E., Kawato, M., Arbib, M. A. (2006).
    Mirror neurons and limitation: A computationally guided review.
    Neural Networks, 19 254-271.
  • Oztop, E., Franklin, D. W., Chaminade, T., Cheng, G. (2005).
    Human-humanoid interaction: Is a humanoid robot perceived as a human?.
    International Journal of Humanoid Robotics, 2, 4, 537-559.
  • Cheng, G., Schaal, S., Atkeson, C. G. (2005).
    Editorial by guest editor.
    International Journal of Humanoid Robotics, 2, 4, Editorial 389-390.
  • Hale, J. G. (2005).
    Practical and theoretical research into humanoid motion and interaction.
    IEEE System, Man, and Cybernetics Society eNewsletter / Internet News Letter.
  • Hale, J. G., Pollick, F. E. (2005).
    ‘Sticky hands’: Learning and generalisation for cooperative physical interactions with a humanoid robot.
    IEEE Transactions on Systems, Man, and Cybernetics : Part C, 35, 4, 512-521.
  • Arbib, M. A., Oztop, E., Zukow-goldring, P. (2005).
    Language and the mirror system: A perception/action based approach to communicative development.
    Cognitie, Creier, Comportament, IX, 3, 239-272.
  • Pollick, F. E., Hale, J. G., Tzoneva, M. (2005).
    Perception of humanoid movement.
    International Journal of Humanoid Robotics, 2, 3, 277-300.
  • Oztop, E., Kawato, M. (2005).
    Conceptual and computational models of mirror neurons.
    Journal of Japanese Neural Network Society, The Brain & Neural Networks, 12, 1 61-73.
  • Gaskett, C., Ude, A., Cheng, G. (2005).
    Hand-eye coordination through endpoint closed-loop and learned endpoint open-loop visual servo control.
    International Journal of Humanoid Robotics, 2, 2, 203-224.
  • Morimoto, J., Doya, K. (2005).
    Robust reinforcement learning.
    Neural Computation, 17, 335-359.
  • Nakanishi, J., Farrell, J. A., Schaal, S. (2005).
    Composite adaptive control with locally weighted statistical learning.
    Neural Networks, 18, 71-90.
  • 松原崇充, 森本淳, 中西淳, 佐藤雅昭, 銅谷賢治 (2005).
    方策こう配法を用いた動的行動則の獲得：2足歩行運動への適用
    電子情報通信学会論文誌, J88-D-II, 1, 53-65.
  • Schaal, S., Sternad, D., Osu, R., Kawato, M. (2004).
    Rhythmic arm movement is not discrete.
    Nature Neuroscience, 7, 10, 1136-1143.
  • Bentivegna, D. C., Atkeson, C. G., Cheng, G. (2004).
    Learning tasks from observation and practice.
    Robotics and Autonomous Systems, 47, 163-169.
  • Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., Kawato, M. (2004).
    Learning from demonstration and adaptation of biped locomotion.
    Robotics and Autonomous Systems, 47, 79-91.
  • Ude, A., Atkeson, C. G., Riley, M. (2004).
    Programming full-body movements for humanoid robots by observation.
    Robotics and Autonomous Systems, 47, 93-108.
  • Billard, A., Epars, Y., Calinon, S., Schaal, S., Cheng, G. (2004).
    Discovering optimal imitation strategies.
    Robotics and Autonomous Systems, 47-2004, 69-77.
  • Bentivegna, D. C., Atkeson, C. G., Ude, A., Cheng, G. (2004).
    Learning to act from observation and practice.
    International Journal of Humanoid Robotics, 1, 4, 585-611.
  • Pollard, N. S. (2004).
    Closure and quality equivalence for efficient synthesis of grasps from examples.
    International Journal of Robotics Research, 23, 6, 595-613.
  • Nakanishi, J., Schaal, S. (2004).
    Feedback error learning and nonlinear learning adaptive control.
    Neural Networks, 17, 1453-1465.
  • Miyamoto, H., Morimoto, J., Doya, K., Kawato, M. (2004).
    Reinforcement learning with via-point representation.
    Neural Networks, 17, 299-305.
  • 中西淳, Ijspeert, A., Schaal, S., Cheng, G. (2004).
    運動学習プリミティブを用いたロボットの見まね学習
    日本ロボット学会誌 特集「ロボットの運動学習」, 22, 2, 165-170.
  • Bentivegna, D. C., Atkeson, C. G., Cheng, G. (2004).
    A framework for learning from observation using primitives.
    Journal of the Robotics Society of Japan, 22, 2, 176-181.
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