Journal papers
- Teramae, T., Matsubara, T., Noda, T., Morimoto, J. (2025/03/01).
Optimizing non-assisted body part movements for robot-assisted therapy
Biomedical Signal Processing and Control, Vol.107, 107817
https://doi.org/10.1016/j.bspc.2025.107817
- Yamamori, S., Morimoto, J. (2025/02/01).
Foundational policy acquisition via multitask learning for motor skill generation
IEEE Transactions on Cognitive and Developmental Systems
https://doi.org/10.1109/TCDS.2025.3543350
- Ichihara, Y., Jinnai, Y., Morimura, T., Abe, K., Ariu, K., Sakamoto, M., Uchibe, E. (2025/02/01).
Evaluation of best-of-n sampling strategies for language model alignment
Transactions on Machine Learning Research, pp.1-46
https://openreview.net/forum?id=H4S4ETc8c9
- Takeda, S., Yamamori, S., Yagi, S., Morimoto, J. (2025/01/01).
An empirical evaluation of a hierarchical reinforcement learning method towards modular robot control
“Artificial Life and Robotics, SWARM Special Issue 2”
https://http://dx.doi.org/10.1007/s10015-025-01003-7
- Matsubara, E., Yagi, S., Goto, Y., Yamamori, S., Morimoto, J. (2025/01/01).
Improvement of fault tolerance of quadruped robots by detecting correlation anomalies in sensor signals
“Artificial Life and Robotics, SWARM Special Issue 2”
https://doi.org/10.1007/s10015-024-00984-1
- Tsuda, A., Manalo, E., Miyai, I., Noda, T. (2024/11/01).
Efficient integration of personal factors into the international classification of functioning, disability, and health (ICF): the importance of emotional and motivational aspects in goal pursuit
Front. Rehabil. Sci., Vol.5
https://doi.org/10.3389/fresc.2024.1450157
- Liu, C., Yagi, S., Yamamori, S., Morimoto, J. (2024/10/01).
Joint-Aware Transformer: An Inter-Joint Correlation Encoding Transformer for Short-Term 3D Human Motion Prediction
IEEE Access, Vol.12, pp.156683-156693
https://doi.org/10.1109/ACCESS.2024.3484660
- 仲田 佳弘、野田 智之 (2024/09/01).
融合型ハイブリッドリニアアクチュエータ
日本フルードパワーシステム学会誌「フルードパワーシステム」, Vol.55 (5), pp.207-211
- Jabbari Asl, H., Uchibe, E. (2024/07/01).
Estimating cost function of expert players in differential games: A model-based method and its data-driven extension
Expert Systems with Applications, Vol.255, Pt.C 124687
https://doi.org/10.1016/j.eswa.2024.124687
- Asai, H., Noda, T., Teramae, T., Morimoto, J. (2024/06/01).
Modeling Inverse Airflow Dynamics Toward Fast Movement Generation Using Pneumatic Artificial Muscle With Long Air Tubes
IEEE/ASME Transactions on Mechatronics, Vol.29 (4), pp.3038-3046
https://doi.org/10.1109/TMECH.2024.3400622
- 野田 智之、温 文、稲邑 哲也 (2024/05/01).
柔らかい支援ロボットによるユーザの運動主体感・自己効力感の向上
公益社団法人計測自動制御学会「計測と制御」, Vol.63 (5), pp.272-279
- Ito, D., Fukuda, M., Hosoi, Y., Hirose, R., Teramae, T., Kamimoto, T., Yamada, Y., Tsuji, T., Noda, T., Kawakami M. (2024/05/01).
Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study
BMC Neurology, Vol.24: 144
https://doi.org/10.1186/s12883-024-03651-x
- Kang, S., Ishihara, K., Sugimoto, N., Morimoto, J. (2023/11).
Curriculum-based humanoid robot identification using large-scale human motion database
Front. Robot. AI, Vol.10
https://doi.org/10.3389/frobt.2023.1282299
- Chiyohara, S., Furukawa, J., Noda, T., Morimoto, J., Imamizu, H. (2023/11).
Proprioceptive short‑term memory in passive motor learning
Scientific Reports, Vol.13 (1), 20826
https://doi.org/10.1038/s41598-023-48101-9
- Kamimoto, T., Hosoi, Y., Tanamachi, K., Yamamoto, R., Yamada, Y., Teramae, T., Noda, T., Kaneko, F., Tsuji, T., Kawakami, M. (2023/08).
Combined Ankle Robot Training and Robot-assisted Gait Training Improved the Gait Pattern of a Patient with Chronic Traumatic Brain Injury
Prog. Rehabil. Med., Vol.8
https://doi.org/10.2490/prm.20230024
- Nakata, Y., Noda, T. (2023/08).
Fusion Hybrid Linear Actuator: Concept and Disturbance Resistance Evaluation
IEEE/ASME Transactions on Mechatronics, Vol.28 (4), pp.2167-2177
https://doi.org/10.1109/tmech.2023.3237725
- Jabbari Asl, H., Uchibe, E. (2023/08).
Online Reinforcement Learning Control of Nonlinear Dynamic Systems: A State-action Value Function Based Solution
Neurocomputing, Vol.544, 126291
https://doi.org/10.1016/j.neucom.2023.126291
- Takai, A., Teramae, T., Noda, T., Ishihara, K., Furukawa, J., Fujimoto, H., Hatakenaka, M., Fujita, N., Jino, A., Hiramatsu, Y., Miyai, I., Morimoto, J. (2023/07).
Development of split-force-controlled body weight support (SF-BWS) robot for gait rehabilitation
Front. Hum. Neurosci., Vol.17, 1197380
https://doi.org/10.3389/fnhum.2023.1197380
- Jabbari Asl, H., Uchibe, E. (2023/07).
Reinforcement learning-based optimal control of unknown constrained-input nonlinear systems using simulated experience
Nonlinear Dynamics, Vol.111 (17), pp.16093–16110
https://doi.org/10.1007/s11071-023-08688-0
- Takai, A., Fu, Q., Doibata, Y., Lisi, G., Tsuchiya, T., Mojtahedi, K., Yoshioka, T., Kawato, M., Morimoto, J., Santello, M. (2023/03/01).
Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions
Scientific Reports, Vol.13 (1), Article No. 3476
https://doi.org/10.1038/s41598-023-29722-6
- Takahashi, Y., Okada, K., Noda, T., Teramae, T., Nakamura, T., Haruyama, K., Okuyama, K., Tsujimoto, K., Mizuno, K., Morimoto, J., Kawakami, M. (2023/01).
Robotized Knee-Ankle-Foot Orthosis-Assisted Gait Training on Genu Recurvatum during Gait in Patients with Chronic Stroke: A Feasibility Study and Case Report
Journal of clinical medicine, Vol.12 (2)
https://doi.org/10.3390/jcm12020415
- Yamanokuchi, T., Kwon, Y., Tsurumine, Y., Uchibe, E., Morimoto, J., Matsubara, T. (2022/10).
Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation
IEEE Robotics and Automation Letters, Vol..7 (4), pp. 8964-8971
https://doi.org/10.1109/lra.2022.3189156
- Uchibe, E. (2022/10).
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy
IEEE Robotics and Automation Letters, Vol.7 (4), pp. 10922-10929
https://doi.org/10.1109/lra.2022.3196139
- Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., Morimoto, J. (2022/08).
Deep learning, reinforcement learning, and world models
Neural Networks, Vol.152, pp.267-275
https://doi.org/10.1016/j.neunet.2022.03.037
- Zhu, L., Chen, Z., Uchibe, E., Matsubara, T. (2022/05).
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Comput. Res. Repos. (CoRR), arXiv:2205.07885
https://doi.org/10.48550/arXiv.2205.07885
- Mavsar, M., Ride, B., Pahic, R., Morimoto J., Ude A. (2022/05/01).
Simulation-aided handover prediction from video using recurrent image-to-motion networks
IEEE Transactions on Neural Networks and Learning Systems, Vol.35 (1), pp.494-506
https://doi.org/10.1109/TNNLS.2022.3175720
- Chujo, Y., Mori, K., Kitawaki, T., Wakida, M., Noda, T., Hase, K. (2022/04).
How to decide the number of gait cycles in different low-pass filters to extract motor modules by non-negative matrix factorization during walking in chronic post-stroke patients
Frontiers in Human Neuroscience April 2022/ Vol.16, Article 803542
https://doi.org/10.3389/fnhum.2022.803542
- Furukawa, J., Okajima, S., An, Q., Nakamura, Y., Morimoto, J. (2022/02).
Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot
IEEE Robotics and Automation Letters, Vol.7/ No.2/pp.3890-3897
https://doi.org/ 10.1109/LRA.2022.3148799
- Takeshi D. Itoh,Koji Ishihara,Jun Morimoto (2022/01).
Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment
Neural Computation (2022) 34 (2): 360–377.
https://doi.org/10.1162/neco_a_01465
- Takai,A., Fu,Q., Doibata,Y., Lisi, G.,Tsuchiya, T., Mojtahedi,K., Yoshioka,T., Kawato M., Morimoto, J., Santello, M. (2021/12).
Leaders are made: Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions.
bioRxiv(Web)
https://doi.org/10.1101/2021.12.09.471486
- Takai,A., Lisi, G., Noda, T., Teramae, T., Imamizu, H., Morimoto,J. (2021/10).
Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training
Frontiers in Neuroscience Vol.15,No.704402
https://doi.org/10.3389/fnins.2021.704402
- Macpherson, T., Matsumoto, M., Gomi, H., Morimoto,J., Uchibe,E., Hidaka, T. (2021/09).
Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control
Neural Networks Vol.144, pp.507-521
https://doi.org/10.1016/j.neunet.2021.09.009
- Furukawa,J., Chiyohara,S., Teramae,T., Takai,A., Morimoto,J. (2021/08).
A collaborative filtering approach toward plug-and-play myoelectric robot control
IEEE Transactions on Human-Machine Systems
https://10.1109/THMS.2021.3098115
- 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
- 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.
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