{"id":2942,"date":"2025-06-02T14:18:53","date_gmt":"2025-06-02T05:18:53","guid":{"rendered":"https:\/\/bicr.atr.jp\/bri\/?page_id=2942"},"modified":"2025-06-02T14:18:53","modified_gmt":"2025-06-02T05:18:53","slug":"journal-papers-2","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/bri\/publication\/journal-papers-2\/","title":{"rendered":"Journal papers"},"content":{"rendered":"<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Teramae, T., Matsubara, T., Noda, T., Morimoto, J.<\/strong> (2025\/03\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Optimizing non-assisted body part movements for robot-assisted therapy<br \/>\nBiomedical Signal Processing and Control, Vol.107, 107817<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.bspc.2025.107817\">https:\/\/doi.org\/10.1016\/j.bspc.2025.107817<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Yamamori, S., Morimoto, J.<\/strong> (2025\/02\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Foundational policy acquisition via multitask learning for motor skill generation<br \/>\nIEEE Transactions on Cognitive and Developmental Systems<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TCDS.2025.3543350\">https:\/\/doi.org\/10.1109\/TCDS.2025.3543350<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ichihara, Y., Jinnai, Y., Morimura, T., Abe, K., Ariu, K., Sakamoto, M., Uchibe, E. <\/strong> (2025\/02\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Evaluation of best-of-n sampling strategies for language model alignment<br \/>\nTransactions on Machine Learning Research, pp.1-46<br \/>\n<a href=\"https:\/\/openreview.net\/forum?id=H4S4ETc8c9\">https:\/\/openreview.net\/forum?id=H4S4ETc8c9<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takeda, S., Yamamori, S., Yagi, S., Morimoto, J.<\/strong> (2025\/01\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">An empirical evaluation of a hierarchical reinforcement learning method towards modular robot control<br \/>\n&#8220;Artificial Life and Robotics, SWARM Special Issue 2&#8221;<br \/>\n<a href=\"https:\/\/http:\/\/dx.doi.org\/10.1007\/s10015-025-01003-7\">https:\/\/http:\/\/dx.doi.org\/10.1007\/s10015-025-01003-7<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, E., Yagi, S., Goto, Y., Yamamori, S., Morimoto, J.<\/strong> (2025\/01\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Improvement of fault tolerance of quadruped robots by detecting correlation anomalies in sensor signals<br \/>\n&#8220;Artificial Life and Robotics, SWARM Special Issue 2&#8221;<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/s10015-024-00984-1\">https:\/\/doi.org\/10.1007\/s10015-024-00984-1<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u53e4\u5ddd \u6df3\u4e00\u6717\u3001\u68ee\u672c \u6df3<\/strong> (2024\/12\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u5916\u9aa8\u683c\u30ed\u30dc\u30c3\u30c8\u306e\u904b\u52d5\u5b66\u7fd2\u624b\u6cd5<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c, Vol.42 (10), pp.947-952<br \/>\n<a href=\"https:\/\/doi.org\/10.7210\/jrsj.42.947\">https:\/\/doi.org\/10.7210\/jrsj.42.947<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u4e0b\u5c71 \u62d3\u771f\u3001\u91ce\u7530 \u667a\u4e4b\u3001\u5bfa\u524d \u9054\u4e5f\u3001\u4ef2\u7530 \u4f73\u5f18<\/strong> (2024\/12\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u30ea\u30cf\u30d3\u30ea\u30c6\u30fc\u30b7\u30e7\u30f3\u7528\u5916\u9aa8\u683c\u30ed\u30dc\u30c3\u30c8\u3092\u7528\u3044\u305f\u6297\u91cd\u529b\u4e0b\u30a2\u30b7\u30b9\u30c8\u4e2d\u306e\u80a9\u95a2\u7bc0\u30a4\u30f3\u30d4\u30fc\u30c0\u30f3\u30b9\u8a08\u6e2c\u306e\u53ef\u80fd\u6027\u306e\u691c\u8a0e \u2015\u7a7a\u96fb\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30a2\u30af\u30c1\u30e5\u30a8\u30fc\u30bf\u306b\u3088\u308b\u4e0a\u80a2\u91cd\u91cf\u88dc\u511f\u3068\u6442\u52d5\u5370\u52a0\u306e\u4e21\u7acb\u2015<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c<br \/>\n<a href=\"https:\/\/www.rsj.or.jp\/pub\/jrsj\/advpub\/241210-10.html\">https:\/\/www.rsj.or.jp\/pub\/jrsj\/advpub\/241210-10.html<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Yamamori, S., Morimoto, J.<\/strong> (2024\/12\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Phase-amplitude reduction-based imitation learning<br \/>\nAdvanced Robotics, Vol.39 (3), pp.156-170<br \/>\n<a href=\"https:\/\/doi.org\/10.1080\/01691864.2024.2441242\">https:\/\/doi.org\/10.1080\/01691864.2024.2441242<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Tsuda, A., Manalo, E., Miyai, I., Noda, T.<\/strong> (2024\/11\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">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<br \/>\nFront. Rehabil. Sci., Vol.5<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fresc.2024.1450157\">https:\/\/doi.org\/10.3389\/fresc.2024.1450157<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2024\/11\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Inverse reinforcement learning methods for linear differential games<br \/>\nSystems &amp; Control Letters, Vol.193, 105936<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.sysconle.2024.105936\">https:\/\/doi.org\/10.1016\/j.sysconle.2024.105936<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Liu, C., Yagi, S., Yamamori, S., Morimoto, J.<\/strong> (2024\/10\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Joint-Aware Transformer: An Inter-Joint Correlation Encoding Transformer for Short-Term 3D Human Motion Prediction<br \/>\nIEEE Access, Vol.12, pp.156683-156693<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2024.3484660\">https:\/\/doi.org\/10.1109\/ACCESS.2024.3484660<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u4ef2\u7530 \u4f73\u5f18\u3001\u91ce\u7530 \u667a\u4e4b<\/strong> (2024\/09\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u878d\u5408\u578b\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30ea\u30cb\u30a2\u30a2\u30af\u30c1\u30e5\u30a8\u30fc\u30bf<br \/>\n\u65e5\u672c\u30d5\u30eb\u30fc\u30c9\u30d1\u30ef\u30fc\u30b7\u30b9\u30c6\u30e0\u5b66\u4f1a\u8a8c\u300c\u30d5\u30eb\u30fc\u30c9\u30d1\u30ef\u30fc\u30b7\u30b9\u30c6\u30e0\u300d, Vol.55 (5), pp.207-211 <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2024\/07\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Estimating cost function of expert players in differential games: A model-based method and its data-driven extension<br \/>\nExpert Systems with Applications, Vol.255, Pt.C 124687<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2024.124687\">https:\/\/doi.org\/10.1016\/j.eswa.2024.124687<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Asai, H., Noda, T., Teramae, T., Morimoto, J.<\/strong> (2024\/06\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Modeling Inverse Airflow Dynamics Toward Fast Movement Generation Using Pneumatic Artificial Muscle With Long Air Tubes<br \/>\nIEEE\/ASME Transactions on Mechatronics, Vol.29 (4), pp.3038-3046<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TMECH.2024.3400622\">https:\/\/doi.org\/10.1109\/TMECH.2024.3400622<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u91ce\u7530 \u667a\u4e4b\u3001\u6e29 \u6587\u3001\u7a32\u9091 \u54f2\u4e5f<\/strong> (2024\/05\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u67d4\u3089\u304b\u3044\u652f\u63f4\u30ed\u30dc\u30c3\u30c8\u306b\u3088\u308b\u30e6\u30fc\u30b6\u306e\u904b\u52d5\u4e3b\u4f53\u611f\u30fb\u81ea\u5df1\u52b9\u529b\u611f\u306e\u5411\u4e0a<br \/>\n\u516c\u76ca\u793e\u56e3\u6cd5\u4eba\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u300c\u8a08\u6e2c\u3068\u5236\u5fa1\u300d, Vol.63 (5), pp.272-279 <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ito, D., Fukuda, M., Hosoi, Y., Hirose, R., Teramae, T., Kamimoto, T., Yamada, Y., Tsuji, T., Noda, T., Kawakami M.<\/strong> (2024\/05\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study<br \/>\nBMC Neurology, Vol.24: 144<br \/>\n<a href=\"https:\/\/doi.org\/10.1186\/s12883-024-03651-x\">https:\/\/doi.org\/10.1186\/s12883-024-03651-x<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2024\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Online estimation of objective function for continuous-time deterministic systems<br \/>\nNeural Networks, Vol.172, 106116<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2024.106116\">https:\/\/doi.org\/10.1016\/j.neunet.2024.106116<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Kang, S., Ishihara, K., Sugimoto, N., Morimoto, J.<\/strong> (2023\/11).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Curriculum-based humanoid robot identification using large-scale human motion database<br \/>\nFront. Robot. AI, Vol.10<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/frobt.2023.1282299\">https:\/\/doi.org\/10.3389\/frobt.2023.1282299<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Chiyohara, S., Furukawa, J., Noda, T., Morimoto, J., Imamizu, H. <\/strong> (2023\/11).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Proprioceptive short\u2011term memory in passive motor learning<br \/>\nScientific Reports, Vol.13 (1), 20826<br \/>\n<a href=\"https:\/\/doi.org\/10.1038\/s41598-023-48101-9\">https:\/\/doi.org\/10.1038\/s41598-023-48101-9<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Kamimoto, T., Hosoi, Y., Tanamachi, K., Yamamoto, R., Yamada, Y., Teramae, T., Noda, T., Kaneko, F., Tsuji, T., Kawakami, M.<\/strong> (2023\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Combined Ankle Robot Training and Robot-assisted Gait Training Improved the Gait Pattern of a Patient with Chronic Traumatic Brain Injury<br \/>\nProg. Rehabil. Med., Vol.8<br \/>\n<a href=\"https:\/\/doi.org\/10.2490\/prm.20230024\">https:\/\/doi.org\/10.2490\/prm.20230024<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Nakata, Y., Noda, T.<\/strong> (2023\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Fusion Hybrid Linear Actuator: Concept and Disturbance Resistance Evaluation<br \/>\nIEEE\/ASME Transactions on Mechatronics, Vol.28 (4), pp.2167-2177<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/tmech.2023.3237725\">https:\/\/doi.org\/10.1109\/tmech.2023.3237725<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2023\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Online Reinforcement Learning Control of Nonlinear Dynamic Systems: A State-action Value Function Based Solution<br \/>\nNeurocomputing, Vol.544, 126291<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neucom.2023.126291\">https:\/\/doi.org\/10.1016\/j.neucom.2023.126291<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takai, A., Teramae, T., Noda, T., Ishihara, K., Furukawa, J., Fujimoto, H., Hatakenaka, M., Fujita, N., Jino, A., Hiramatsu, Y., Miyai, I., Morimoto, J.<\/strong> (2023\/07).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Development of split-force-controlled body weight support (SF-BWS) robot for gait rehabilitation<br \/>\nFront. Hum. Neurosci., Vol.17, 1197380<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnhum.2023.1197380\">https:\/\/doi.org\/10.3389\/fnhum.2023.1197380<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2023\/07).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Reinforcement learning-based optimal control of unknown constrained-input nonlinear systems using simulated experience<br \/>\nNonlinear Dynamics, Vol.111 (17), pp.16093\u201316110<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/s11071-023-08688-0\">https:\/\/doi.org\/10.1007\/s11071-023-08688-0<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takai, A., Fu, Q., Doibata, Y., Lisi, G., Tsuchiya, T., Mojtahedi, K., Yoshioka, T., Kawato, M., Morimoto, J., Santello, M.<\/strong> (2023\/03\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions<br \/>\nScientific Reports, Vol.13 (1), Article No. 3476<br \/>\n<a href=\"https:\/\/doi.org\/10.1038\/s41598-023-29722-6\">https:\/\/doi.org\/10.1038\/s41598-023-29722-6<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takahashi, Y., Okada, K., Noda, T., Teramae, T., Nakamura, T., Haruyama, K., Okuyama, K., Tsujimoto, K., Mizuno, K., Morimoto, J., Kawakami, M.<\/strong> (2023\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Robotized Knee-Ankle-Foot Orthosis-Assisted Gait Training on Genu Recurvatum during Gait in Patients with Chronic Stroke: A Feasibility Study and Case Report<br \/>\nJournal of clinical medicine, Vol.12 (2)<br \/>\n<a href=\"https:\/\/doi.org\/10.3390\/jcm12020415\">https:\/\/doi.org\/10.3390\/jcm12020415<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Jabbari\u00a0Asl, H.,\u00a0Uchibe, E.<\/strong> (2022\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Online Data-Driven Inverse Reinforcement Learning for Deterministic Systems<br \/>\n2022 IEEE Symposium Series on Computational Intelligence (SSCI)<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/ssci51031.2022.10022226\">https:\/\/doi.org\/10.1109\/ssci51031.2022.10022226<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Yamanokuchi, T., Kwon, Y., Tsurumine, Y., Uchibe, E., Morimoto, J., Matsubara, T.<\/strong> (2022\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Randomized-to-Canonical Model Predictive Control for Real-World Visual Robotic Manipulation<br \/>\nIEEE Robotics and Automation Letters, Vol..7 (4), pp. 8964-8971<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/lra.2022.3189156\">https:\/\/doi.org\/10.1109\/lra.2022.3189156<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Uchibe, E.<\/strong> (2022\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Model-Based Imitation Learning Using Entropy Regularization of Model and Policy<br \/>\nIEEE Robotics and Automation Letters, Vol.7 (4), pp. 10922-10929<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/lra.2022.3196139\">https:\/\/doi.org\/10.1109\/lra.2022.3196139<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., Morimoto, J.<\/strong> (2022\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Deep learning, reinforcement learning, and world models<br \/>\nNeural Networks, Vol.152, pp.267-275<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2022.03.037\">https:\/\/doi.org\/10.1016\/j.neunet.2022.03.037<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Zhu,\u00a0L., Chen,\u00a0Z., Uchibe,\u00a0E., Matsubara, T.<\/strong> (2022\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">q-Munchausen Reinforcement Learning<br \/>\nComput. Res. Repos. (CoRR), arXiv:2205.07467<br \/>\n<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2205.07467\">https:\/\/doi.org\/10.48550\/arXiv.2205.07467<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Zhu,\u00a0L., Chen,\u00a0Z., Uchibe,\u00a0E., Matsubara, T.<\/strong> (2022\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning<br \/>\nComput. Res. Repos. (CoRR), arXiv:2205.07885<br \/>\n<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2205.07885\">https:\/\/doi.org\/10.48550\/arXiv.2205.07885<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Mavsar, M.,\u00a0Ride, B., Pahic, R., Morimoto J.,\u00a0Ude A.<\/strong> (2022\/05\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Simulation-aided handover prediction from video using recurrent image-to-motion networks<br \/>\nIEEE Transactions on Neural Networks and Learning Systems, Vol.35 (1), pp.494-506<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TNNLS.2022.3175720\">https:\/\/doi.org\/10.1109\/TNNLS.2022.3175720<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Chujo, Y., Mori, K., Kitawaki, T., Wakida, M., Noda, T., Hase, K.<\/strong> (2022\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">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<br \/>\nFrontiers in Human Neuroscience April 2022\/ Vol.16, Article 803542<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnhum.2022.803542\">https:\/\/doi.org\/10.3389\/fnhum.2022.803542<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa, J., Okajima, S., An, Q., Nakamura, Y., Morimoto, J.<\/strong> (2022\/02).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot<br \/>\nIEEE Robotics and Automation Letters, Vol.7\/ No.2\/pp.3890-3897<br \/>\n<a href=\"https:\/\/doi.org\/ 10.1109\/LRA.2022.3148799\">https:\/\/doi.org\/ 10.1109\/LRA.2022.3148799<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takeshi D. Itoh,Koji Ishihara,Jun Morimoto<\/strong> (2022\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment<br \/>\nNeural Computation (2022) 34 (2): 360\u2013377.<br \/>\n<a href=\"https:\/\/doi.org\/10.1162\/neco_a_01465\">https:\/\/doi.org\/10.1162\/neco_a_01465<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takai,A., Fu,Q., Doibata,Y., Lisi, G.,Tsuchiya, T., Mojtahedi,K., Yoshioka,T., Kawato M., Morimoto, J., Santello, M.<\/strong> (2021\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Leaders are made: Learning acquisition of consistent leader-follower relationships depends on implicit haptic interactions.<br \/>\nbioRxiv(Web)<br \/>\n<a href=\"https:\/\/doi.org\/10.1101\/2021.12.09.471486\">https:\/\/doi.org\/10.1101\/2021.12.09.471486<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Takai,A., Lisi, G., Noda, T., Teramae, T., Imamizu, H., Morimoto,J.<\/strong> (2021\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training<br \/>\nFrontiers in Neuroscience Vol.15,No.704402<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnins.2021.704402\">https:\/\/doi.org\/10.3389\/fnins.2021.704402<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u77f3\u539f\u5f18\u4e8c\u3001\u3000\u68ee\u672c\u6df3<\/strong> (2021\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u5168\u8eab\u306e\u30c0\u30a4\u30ca\u30df\u30af\u30b9\u3092\u8003\u616e\u3057\u305f\u6700\u9069\u5236\u5fa1<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c 39\u5dfb 7\u53f7 p. 597-600<br \/>\n<a href=\"https:\/\/doi.org\/10.7210\/jrsj.39.597\">https:\/\/doi.org\/10.7210\/jrsj.39.597<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u5185\u90e8\u82f1\u6cbb<\/strong> (2021\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u9806\u30fb\u9006\u5f37\u5316\u5b66\u7fd2\u3092\u7528\u3044\u305f\u751f\u6210\u7684\u6a21\u5023\u5b66\u7fd2<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c 39\u5dfb 7\u53f7 p. 617-620<br \/>\n<a href=\"https:\/\/doi.org\/10.7210\/jrsj.39.617\">https:\/\/doi.org\/10.7210\/jrsj.39.617<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Macpherson, T., Matsumoto, M., Gomi, H., Morimoto,J., Uchibe,E., Hidaka, T.<\/strong> (2021\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control<br \/>\nNeural Networks Vol.144, pp.507-521<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2021.09.009\">https:\/\/doi.org\/10.1016\/j.neunet.2021.09.009<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Uchibe,E., Doya,K.<\/strong> (2021\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Forward and inverse reinforcement learning sharing network weights and hyperparameters<br \/>\nNeural Networks Vol.144, pp.138-153<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2021.08.017\">https:\/\/doi.org\/10.1016\/j.neunet.2021.08.017<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa,J., Chiyohara,S., Teramae,T., Takai,A., Morimoto,J.<\/strong> (2021\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A collaborative filtering approach toward plug-and-play myoelectric robot control<br \/>\nIEEE Transactions on Human-Machine Systems<br \/>\n<a href=\"https:\/\/10.1109\/THMS.2021.3098115\">https:\/\/10.1109\/THMS.2021.3098115<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa,J., Morimoto,J.<\/strong>(2021\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Composing an assistive control strategy based on linear bellman combination from estimated user&#8217;s motor goal<br \/>\n<em>IEEE Robotics and Automation Letters Vol.6,No.2,pp.1051-1058<\/em><br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/LRA.2021.3051562\">https:\/\/doi.org\/10.1109\/LRA.2021.3051562<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Wang,J., Elfwing,S., Uchibe,E.<\/strong>(2020\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Modular deep reinforcement learning from reward and punishment for robot navigation<br \/>\n<em>Neural Networks Vol.135,pp.115-126<\/em><br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2020.12.001\">https:\/\/doi.org\/10.1016\/j.neunet.2020.12.001<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Teramae,T., Matsubara,T., Noda,T., Morimoto,J.<\/strong>(2020\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Quaternion-based trajectory optimization of human postures for inducing target<br \/>\n<em>IEEE Robotics and Automation Letters Vol.5, No.4, pp.6607-6614<\/em><br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/LRA.2020.3015460\">https:\/\/doi.org\/10.1109\/LRA.2020.3015460<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Uchibe,E., Doya,K.<\/strong>(2020\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Imitation learning based on entropy-regularized forward and inverse reinforcement learning<br \/>\n<em>arXiv(Web)<\/em><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2008.07284\">https:\/\/arxiv.org\/abs\/2008.07284<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Pahic,R., Ridge,B., Gams,A., Morimoto,J., Ude,A.<\/strong>(2020\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Training of deep neural networks for the generation of dynamic movement primitives<br \/>\n<em>Neural Netowrks Vol.127, pp.121-131<\/em><br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2020.04.010\">https:\/\/doi.org\/10.1016\/j.neunet.2020.04.010<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Maeda,G., Koc, O., Morimoto,J.<\/strong>(2020\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Phase portraits as movement primitives for fast humanoid robot control<br \/>\n<em>Neural Networks Vol.129, pp.109-122<\/em><br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2020.04.007\">https:\/\/doi.org\/10.1016\/j.neunet.2020.04.007<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Lioutikov,R., Maeda,G., Veiga,F., Kersting,K., Petes,J.<\/strong>(2020\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning attribute grammars for movement primitive sequencing<br \/>\n<a href=\"https:\/\/doi.org\/10.1177\/0278364919868279\">https:\/\/doi.org\/10.1177\/0278364919868279<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Celemin,C., Maeda,G., Ruiz-del-solar,J., Peters,J., Kober,J.<\/strong>(2019\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Reinforcement learning of motor skills using policy search and human corrective<br \/>\n<a href=\"https:\/\/doi.org\/10.1177\/0278364919871998\">https:\/\/doi.org\/10.1177\/0278364919871998<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ohnishi,S., Uchibe,E., Yamaguchi,Y., Nakanishi,K., Yasui,Y., Ishii,S.<\/strong> (2019\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Constrained deep Q-learning gradually approaching ordinary Q-learning<br \/>\nFrontiers in Neurorobotics Vol.13, Article 103<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2019.00103\">https:\/\/doi.org\/10.3389\/fnbot.2019.00103<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ishihara,K., Itoh D.T., Morimoto,J. <\/strong> (2019\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Full-body optimal control toward versatile and agile behaviors in a humanoid robot<br \/>\n&#8220;IEEE Robotics and Automation Letters Vol.5,No.1,pp.119-126&#8221;<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/LRA.2019.2947001\">https:\/\/doi.org\/10.1109\/LRA.2019.2947001<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Iwane,F., Lisi,G., Morimoti,J.<\/strong> (2019\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">EEG sensorimotor correlates of speed during forearm passive movements<br \/>\nIEEE Transactions on Neural Systems and Rehabilitation Engineering Vol.27,Issue9, pp.1667-1675<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TNSRE.2019.2934231\">https:\/\/doi.org\/10.1109\/TNSRE.2019.2934231<\/a><br \/>\n<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hamaya,M., Matsubara,T., Teramae,T., Noda,T., Morimoto,J.<\/strong> (2019\/06).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Design of physical user-robot interactions for model identification of soft<br \/>\nInternational Journal of Robotics Research<br \/>\n<a href=\"https:\/\/doi.org\/10.1177\/0278364919853618\">https:\/\/doi.org\/10.1177\/0278364919853618<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa,J., Morimoto,J.<\/strong>(2019\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">An optimal assistive control strategy based on user&#8217;s motor goal estimation<br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/1909.02288\">https:\/\/arxiv.org\/abs\/1909.02288<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ugurlu B., Forni,P., Doppmann,C., Sariyiliz,E., Morimoto,J.<\/strong> (2019\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Stable control of force, position, and stiffness for robot joints powered via pneumatic muscles<br \/>\nIEEE Transactions on Industrial Informatics<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TII.2019.2916228\">https:\/\/doi.org\/10.1109\/TII.2019.2916228<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Petric,T., Peternel,L., Morimoto,J., Babic,J.<\/strong> (2019\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Assistive arm-exoskeleton control based on human muscular manipulability<br \/>\nFrontiers in Neurorobotics Vol.13, Article 30<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2019.00030\">https:\/\/doi.org\/10.3389\/fnbot.2019.00030<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Tsurumine, Y.,Cui,Y., Uchibe,E., Matsubara,T.<\/strong> (2018\/11).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Deep reinforcement learning with smooth policy update: application to robotic cloth manipulation<br \/>\nRobotics and Autonomous Systems Vol.112, pp.72-83<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.robot.2018.11.004\">https:\/\/doi.org\/10.1016\/j.robot.2018.11.004<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Teramae,T., Ishihara, K., Babic,J., Morimoto,J. Oztop,E.<\/strong> (2018\/11).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Human-in-the-loop control and task learning for pneumatically actuated muscle based robots<br \/>\nFrontiers in Human-in-the-Loop Robot Control and Learning\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000Vol.12, Article 71<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2018.00071\">https:\/\/doi.org\/10.3389\/fnbot.2018.00071<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Uchibe,E.<\/strong> (2018\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Cooperative and competitive reinforcement and imitation learning for a mixture of heterogeneous learning modules<br \/>\nFrontiers in Neurorobotics Vol.12,Artcle61<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2018.00061\">https:\/\/doi.org\/10.3389\/fnbot.2018.00061<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ewerton,M., Rother, D., Weimar, O.J., Kollegger,G., Wiemeyer J., Peters, J., Maeda,G<\/strong> (2018\/05).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Assisting movement training and execution with visual and haptic feedback<br \/>\nFrontiers in Robotics and AI Vol.24, Article 24, pp.1-19<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2018.00024\">https:\/\/doi.org\/10.3389\/fnbot.2018.00024<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Koc,O, Maeda,G., Peters, J.<\/strong> (2018\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Online optimal trajectory generation for robot table tennis<br \/>\nRobotics and Autonomous Systems Vol.105, pp.121-137<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.robot.2018.03.012\">https:\/\/doi.org\/10.1016\/j.robot.2018.03.012<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Lisi,G., Rivela,D., Takai,A., Morimoto,J.<\/strong> (2018\/02).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Markov switching model for quick detection of event related desynchronization in EEG<br \/>\nFrontiers in Neuroscience-Neuroprosthetics Vol.12, Article 24<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnins.2018.00024\">https:\/\/doi.org\/10.3389\/fnins.2018.00024<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ishihara,K., Morimoto,J.<\/strong> (2018\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">An optimal control strategy for hybrid actuator systems: application to an artificial muscle with electric motor assist<br \/>\nNeural Networks Vol.99, pp.92-100<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2017.12.010\">https:\/\/doi.org\/10.1016\/j.neunet.2017.12.010<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Gasper,T., Nemec,B., Morimoto,J., Ude,A.<\/strong> (2017\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Skill learning and action recognition by arc-length dynamic movement primitives<br \/>\nRobotics and Autonomous Systems Vol.100, pp.225-235<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.robot.2017.11.012\">https:\/\/doi.org\/10.1016\/j.robot.2017.11.012<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hamaya,M., Tatsubara,T., Noda,T., Teramae,T., Morimoto,J.<\/strong> (2017\/11).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning assistive strategies for exoskeleton robots from user-robot physical interaction<br \/>\nPattern Recognition Letters Vol.99, pp.67-76<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.patrec.2017.04.007\">https:\/\/doi.org\/10.1016\/j.patrec.2017.04.007<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Kozono,T., Uchibe,E., Doya,K.<\/strong> (2017\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Unifying value iteration, advantage learning, and dynamic policy programming<br \/>\narXiv.org(Web) arXiv:1710.10866<br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/1710.10866\">https:\/\/arxiv.org\/abs\/1710.10866<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Kinjo,K., Uchibe,E., Doya,K.<\/strong> (2017\/10).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Robustness of linearly solvable markov games employing inaccurate dynamics model<br \/>\nJournal of Artificial Life and Robotics Vol.23,Issue1, pp.1-9<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/s10015-017-0401-2\">https:\/\/doi.org\/10.1007\/s10015-017-0401-2<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Uchibe,E.<\/strong> (2017\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Model-free deep inverse reinforcement learning by logistic regression<br \/>\nNeural Processing Letters Vol.47, Issue 3, pp.891-905<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/s11063-017-9702-7\">https:\/\/doi.org\/10.1007\/s11063-017-9702-7<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Teramae,T., Noda,T., Morimoto,J.<\/strong> (2017\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">EMG-based model predictive control for physical human-robot interaction: Application for assist-as-needed control<br \/>\nIEEE Robotics and Automation Letters(RA-L) Vol.3, No.1, pp.210-217<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/LRA.2017.2737478\">https:\/\/doi.org\/10.1109\/LRA.2017.2737478<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>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.<\/strong> (2017\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Identifying melancholic depression biomarker using whole-brain functional connectivity<br \/>\narXiv.org(Web) arXiv:1704.01039<br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/1704.01039\">https:\/\/arxiv.org\/abs\/1704.01039<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa,J., Noda,T., Teramae,T., Morimoto,J.<\/strong> (2017\/04).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Human movement modeling to detect biosignal sensor failures for myoelectric assistive robot control<br \/>\nIEEE Transactions on Robotics Vol.33, No.4, pp.846-856<br \/>\n<a href=\"https:\/\/ieeexplore.ieee.org\/document\/7906627\">https:\/\/ieeexplore.ieee.org\/document\/7906627<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Wang,J., Uchibe,E., Doya,K.<\/strong> (2017\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Adaptive baseline enhances EM-based policy search: validation in a view-based positioning task of a smartphone balancer<br \/>\nFrontiers in Neurorobotics(Web) Vol.11, Article 1<br \/>\n<a href=\"https:\/\/doi.org\/10.3389\/fnbot.2017.00001\">https:\/\/doi.org\/10.3389\/fnbot.2017.00001<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Tangkaratta,V., Morimoto,J., Sugiyama,M.<\/strong> (2016\/12).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Model-based reinforcement learning with dimension reduction<br \/>\nNeural Networks Vol.84,pp.1-16<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2016.08.005\">https:\/\/doi.org\/10.1016\/j.neunet.2016.08.005<\/a><br \/>\n<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Elfwing,S., Uchibe,E., Doya,K.<\/strong> (2016\/08).<\/span><br \/>\n<span style=\"font-family: helvetica;\">From free energy to expected energy:improving energy-based value function approximation in reinforcement learning<br \/>\nNeural Networks Vol.84,pp.17-27<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2016.07.013\">https:\/\/doi.org\/10.1016\/j.neunet.2016.07.013<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u5185\u90e8\u82f1\u6cbb<\/strong> (2016\/03).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u7dda\u5f62\u53ef\u89e3\u30de\u30eb\u30b3\u30d5\u6c7a\u5b9a\u904e\u7a0b\u3092\u7528\u3044\u305f\u9806\u30fb\u9006\u5f37\u5316\u5b66\u7fd2<br \/>\n\u65e5\u672c\u795e\u7d4c\u56de\u8def\u5b66\u4f1a\u8a8c Vol.23, No.1, pp.2-13<br \/>\n<a href=\"https:\/\/doi.org\/10.3902\/jnns.23.2\">https:\/\/doi.org\/10.3902\/jnns.23.2<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Sugimoto,N., Tangkaratta,V., Wensveen,T., Zhao,T., Sugiyama,M., Morimoto,J.<\/strong> (2016\/02).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Trial and error: using previous experiences as simulation models in humanoid motor learning<br \/>\nIEEE Robotics and Automation Magazine Vol.23, Issue 1, pp.96-105<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/MRA.2015.2511681\">https:\/\/doi.org\/10.1109\/MRA.2015.2511681<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Peternel,L., Noda,T., Petric,T., Ude,A., Morimoto,J., Babic,J.<\/strong> (2016\/02).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation<br \/>\nPLoS ONE Vol.11, Issue2, e0148942<br \/>\n<a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0148942\">https:\/\/doi.org\/10.1371\/journal.pone.0148942<\/a> <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Gams,A., Petric,T., Do,M., Nemec,B., Morimoto,J., Asfour,T., Ude,A.<\/strong> (2016\/01).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Adaptation and coaching of periodic motion primitives through physical and visual interaction<br \/>\nRobotics and Autonomous Systems Vol.75, Part B, pp.340-351<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.robot.2015.09.011\">https:\/\/doi.org\/10.1016\/j.robot.2015.09.011<\/a><br \/>\n<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara,T., Uchikata, A., Morimoto, J.<\/strong> (2015\/09).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Spatiotemporal synchronization of biped walking patterns with multiple external inputs by style-phase adaptation<br \/>\nBiological Cybernetics\uff08Web\uff09Vol.109, Issue 6, pp.597-610<br \/>\n<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00422-015-0663-5\">https:\/\/link.springer.com\/article\/10.1007\/s00422-015-0663-5<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ugurlu,B., Doppmann,C., Hamaya,M., Forni,P., Teramae,T., Noda,T., Morimoto,J.<\/strong> (2015\/06).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Variable ankle stiffness improves balance control:experiments on a bipedal exoskeleton<br \/>\nIEEE Transactions on Mechatronics Vol.21,No.1,pp.79-87<br \/>\n<a href=\"https:\/\/doi.org\/10.1109\/TMECH.2015.2448932\">https:\/\/doi.org\/10.1109\/TMECH.2015.2448932<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Tangkaratt, V., Mori, S., Zhao, T., Mmorimoto, J., Sugiyama, M.<\/strong> (2015).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation<br \/>\nNeural Networks, Vol.57, 128-140<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Kawato, M.<\/strong> (2015).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Creating the brain and interacting with the brain:an Integrated approach to understanding the brain<br \/>\nJournal of the Royal Society Interface, Vol.12, 104, 20141250<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa, J., Noda, T., Teramae, T., Morimoto, J.<\/strong> (2015).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Fault tolerant approach for biosignal-based robot control<br \/>\nAdvanced Robotics, Vol.29, 7, 505-514.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Lisi, G., Morimoto, J.<\/strong> (2015).<\/span><br \/>\n<span style=\"font-family: helvetica;\">EEG single-trial detection of gait speed changes treadmill walk<br \/>\nPLoS ONE, Vol.10, 4, 1-28.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Bouyarmane, K., Vaillant, J., Sugimoto, N., Keith, F., Furukawa, J., Morimoto, J.<\/strong> (2014).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Brain-machine interfacing control of whole-body humanoid motion<br \/>\nFrontiers in Systems Neurosciences, Vol.8, 138, 1-10.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Lisi, G., Noda, T., Morimoto, J.<\/strong> (2014).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Decoding the ERD\/ERS: influence of afferent input induced by a leg assistive robot<br \/>\nFrontiers in Neuroscience, Vol.8, 85, 1-12.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Furukawa, J., Noda, T., Teramae, T., Morimoto, J.<\/strong> (2014).<\/span><br \/>\n<span style=\"font-family: helvetica;\">An EMG-driven weight support system with pneumatic artificial muscles<br \/>\nIEEE Systems Journal, DOI:10.1109\/JSYST.2014.2330376.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ugurlu, B., Saglia, J., Tsagarakis, N., Morfey, S., Caldwell, D.<\/strong> (2014).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Bipedal hopping pattern generation for passively compliant humanoids:exploiting the resonance<br \/>\nIEEE Transactions on Industrial Electronics, Vol.61, 10, 5431-5443.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u68ee\u672c\u6df3, \u6749\u672c\u5fb3\u548c<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u9ad8\u6b21\u5143\u30fb\u5b9f\u74b0\u5883\u306b\u304a\u3051\u308b\u5f37\u5316\u5b66\u7fd2<br \/>\n\u8a08\u6e2c\u3068\u5236\u5fa1, Vol.52, 8, 742-748.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Zhao, T., Hachiya, H., Tangkaratt, V., Morimoto, J., Sugiyama, M.<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Efficient sample reuse in policy gradients with parameter-based exploration<br \/>\nNeural Computation, Vol.25, 6, 1512-1547.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ariki, Y., Hyon, S., Morimoto, J.<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Extraction of primitive representation from captured human movements and measured ground reaction force to generate physically consistent imitated behaviors<br \/>\nNeural Networks, Vol.40, 32-43. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, T., Morimoto, J.<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Bilinear modeling of EMG Signals to extract user-independent features for multiuser myoelectric interface<br \/>\nIEEE Transactions on Biomedical Engineering, Vol.60, 8, 2205-2213. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u677e\u539f\u5d07\u5145, \u68ee\u672c\u6df3<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u591a\u91cd\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u89e3\u6790\u306e\u305f\u3081\u306e\u6b63\u6e96\u591a\u91cd\u6574\u5217\u6cd5<br \/>\n\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8cD, Vol.J96-D, 2, 298-305. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Schiebener, D., Morimoto, J., Asfour, T., Ude, A.<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Integrating visual perception and manipulation for autonomous learning of object representations<br \/>\nAdaptive Behavior, Vol.21, 5, 328-345. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Manoonpong, P., Kolodziejski, C., Worgotter, F., Morimoto, J.<\/strong> (2013).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Combining correlation-based and reward-based learning in neural control for policy improvement<br \/>\nAdvances in Complex Systems, Vol.16, 2&amp;3, 1350015-pp.1-38. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Forte, D., Gams, A., Morimoto, J., Ude, A.<\/strong> (2012).<\/span><br \/>\n<span style=\"font-family: helvetica;\">On-line motion synthesis and adaptation using a trajectory database<br \/>\nRobotics and Autonomous Systems, Vol.60, 10, 1327-1339. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u5185\u65b9\u7ae0\u96c5, \u677e\u539f\u5d07\u5145, \u68ee\u672c\u6df3<\/strong> (2012).<\/span><br \/>\n<span style=\"font-family: helvetica;\">\u30b9\u30bf\u30a4\u30eb-\u4f4d\u76f8\u9069\u5fdc\u306b\u57fa\u3065\u304f\u5468\u671f\u904b\u52d5\u306e\u6642\u7a7a\u9593\u540c\u671f:2\u8db3\u6b69\u884c\u904b\u52d5\u3078\u306e\u9069\u7528<br \/>\n\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u548c\u6587\u8ad6\u6587\u8a8cD, J95-D, 7, 1476-1487. <\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Sugimoto, N., Morimoto, J., Hyon, S., Kawato, M.<\/strong> (2012).<\/span><br \/>\n<span style=\"font-family: helvetica;\">The eMOSAIC model for humanoid robot control.<br \/>\nNeural Networks, 29-30, 8-9.<br \/>\n<a href=\"https:\/\/doi.org\/10.1016\/j.neunet.2012.01.002\">https:\/\/doi.org\/10.1016\/j.neunet.2012.01.002<\/a><\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Sugimoto, N., Haruno, M., Doya, K., Kawato, M.<\/strong> (2012).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Mosaic for multiple-reward environments.<br \/>\nNeural Computation, 24, 3, 577-606.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, T., Hyon, S., Morimoto, J.<\/strong> (2012).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Real-time stylistic prediction for whole-body human motions.<br \/>\nNeural Networks, 25, 191-199.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, T., Hyon, S., Morimoto, J.<\/strong> (2011).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning parametric dynamic movement primitives from multiple demonstrations.<br \/>\nNeural Networks, 24, Issue 5, 493-500.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u677e\u539f\u5d07\u5145, \u7384\u76f8\u660a, \u68ee\u672c\u6df3<\/strong> (2011).<br \/>\n\u500b\u6027\u3092\u8003\u616e\u3057\u305f\u5468\u671f\u7684\u5168\u8eab\u904b\u52d5\u306e\u4e88\u6e2c.<br \/>\n\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8c, J94-D, 1, 344-355.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ude, A., Gams, A., Asfour, T., Morimoto, J.<\/strong> (2010).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Task-specific generalization of discrete and periodic dynamic movement primitives.<br \/>\nIEEE Transactions on Robotics, 26, 5, 800-815.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u7384\u76f8\u660a, \u91cc\u5b87\u660e\u5143<\/strong> (2010).<br \/>\n\u53ef\u5909\u91cd\u529b\u74b0\u5883\u306b\u304a\u3051\u308b\u5168\u8eab\u904b\u52d5\u5236\u5fa1\u3068\u7b49\u8eab\u5927\u30d2\u30c8\u578b\u30ed\u30dc\u30c3\u30c8\u3092\u7528\u3044\u305f\u691c\u8a3c.<br \/>\n\u30d0\u30a4\u30aa\u30e1\u30ab\u30cb\u30ba\u30e0\u5b66\u4f1a\u8a8c, 34, 1, 5-11.<\/li>\n<\/ul>\n<ul>\n<li><strong>\u7384\u76f8\u660a<\/strong> (2009).<br \/>\n\u6e96\u9759\u7684\u306b\u7372\u5f97\u3057\u305f\u95a2\u7bc0\u8ecc\u9053\u3092\u5229\u7528\u3057\u3066\u52d5\u7684\u306a\u985e\u4f3c\u904b\u52d5\u3092\u9010\u6b21\u7684\u306b\u5b66\u7fd2\u3059\u308b\u65b9\u6cd5.<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c, 27, 9, 1025-1028.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hyon, S.<\/strong> (2009).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A motor control strategy with virtual musculoskeletal systems for compliant anthropomorphic robots.<br \/>\nIEEE\/ASME Transactions on Mechatronics, 14, 6, 677-688.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Atkeson, C. G.<\/strong> (2009).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Nonparametric representation of an approximated poincare map for learning biped locomotion.<br \/>\nAutonomous Robots, Vol.27, 2, 131-144.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u7384\u76f8\u660a<\/strong> (2009).<br \/>\n\u8907\u6570\u306e\u63a5\u5730\u90e8\u5206\u3068\u5197\u9577\u95a2\u7bc0\u3092\u6709\u3059\u308b\u30d2\u30e5\u30fc\u30de\u30ce\u30a4\u30c9\u30ed\u30dc\u30c3\u30c8\u306e\u53d7\u52d5\u6027\u306b\u57fa\u3065\u304f\u6700\u9069\u63a5\u89e6\u529b\u5236\u5fa1.<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c, 27, 2, 178-187.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hyon, S.<\/strong> (2009).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Compliant terrain adaptation for biped humanoids without measuring ground surface and contact forces.<br \/>\nIEEE Transactions on Robotics, 25, 1, 171-178.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ude, A., Omrcen, D., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Making object learning and recognition an active process.<br \/>\nInternational Journal of Humanoid Robotics, 5, 2, 267-286.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hale, J. G., Hohl, B., Hyon, S., Matsubara, T., Moraud, E. M., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Highly precise dynamic simulation environment for humanoid robots.<br \/>\nAdvanced Robotics: Special Issue on Humanoid Technologies and Systems, 22, 10, 1075-1105.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, T., Morimoto, J., Nakanishi, J., Hyon, S., Hale, J. G., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning to acquire whole-body humanoid center of mass movements to achieve dynamic tasks.<br \/>\nAdvanced Robotics: Special Issue on Humanoid Technologies and Systems, 22, 10, 1125-1142.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Operational Space Control: A theoretical and empirical comparison.<br \/>\nInternational Journal of Robotics Research, 27, 6, 737-757.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u7384\u76f8\u660a, \u85e4\u672c\u5065\u6cbb<\/strong> (2008).<br \/>\n\u30cf\u30df\u30eb\u30c8\u30f3\u529b\u5b66\u7cfb\u306e\u5bfe\u79f0\u8ecc\u9053\u65cf\u3068\uff12\u8db3\u6b69\u884c\u306e\u5927\u57df\u7684\u6b69\u5bb9\u751f\u6210\u3078\u306e\u5fdc\u7528<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c, 26, 4, 372-380.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Moren, J., Ude, A., Koene, A., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Biologically based top-down attention modulation for humanoid interactions.<br \/>\nInternational Journal of Humanoid Robotics, 5, 1, 3-24.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Endo, G., Morimoto, J., Matsubara, T., Nakanishi, J., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning CPG-based biped locomotion with a policy gradient method: Application to a humanoid robot.<br \/>\nThe International Journal of Robotics Research, Special Issue on Machine Learning in Robotics, 27, 2, 213-228.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Chaminade, T., Oztop, E., Cheng, G., Kawato, M.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">From self-observation to imitation: Visuomotor association on a robotic hand.<br \/>\nBrain Research Bulletin, 75, 775-784.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Endo, G., Nakanishi, J., Cheng, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A biologically inspired biped locomotion strategy for humanoid robots: Modulation of simple sinusoidal patterns by a coupled oscillator model.<br \/>\nIEEE Transaction on Robotics, 24, 1, 185-191.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Cheng, G., Metta, G., Cannata, G., Sandini, G.<\/strong> (2008).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Humanoid technologies:&#8221;Know-how&#8221;.<br \/>\nRobotics and Autonomous Systems, 56, Issue 1, 1-3.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u4f50\u85e4\u8a13\u5fd7, \u85e4\u672c\u5065\u6cbb, \u7384\u76f8\u660a<\/strong> (2007).<br \/>\n\u30cf\u30df\u30eb\u30c8\u30f3\u7cfb\u306e\u5909\u5206\u5bfe\u79f0\u6027\u306b\u57fa\u3065\u304f1\u811a\u30ed\u30dc\u30c3\u30c8\u306e\u6700\u9069\u6b69\u5bb9\u751f\u6210<br \/>\n\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u8ad6\u6587\u96c6, 3, 12, 1103-1110.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Peters, J., Mistry, M., Udwadia, F., Nakanishi, J., Schaal, S.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A unifying framework for robot control with redundant DOFs.<br \/>\nAutonomous Robots, Vil.24, 1, 1-12.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hyon, S., Hale, J. G., Cheng, G.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces.<br \/>\nIEEE Transactions on Robotics, 23, 5, 884-898.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Koene, A., Arnold, D., Johnston, A.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Bimodal sensory discrimination is finer than dual single modality discrimination.<br \/>\nJournal of Vision, 7, 11, Article14, 1-11.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Cheng, G., Hyon, S., Morimoto, J., Ude, A., Hale, J. G., Colvin, G., Scroggin, W., Jacobsen, S. C.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">CB: A humanoid research platform for exploring neuroscience.<br \/>\nJournal of Advance Robotics, 21, 10, 1097-1114.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ude, A., Moren, J., Cheng, G.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Visual attention and distributed processing of visual information for the control of humanoid robots.<br \/>\nHumanoid Robots Human-like Machines (International Journal of Advanced Robotic Systems), 423-436.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Atkeson, C.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning biped locomotion: Application of poincare-map-based reinforcement learning.<br \/>\nIEEE Robotics and Automation Magazine, 14, 2, 41-51.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Koene, A. R., Zhaoping, L.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Feature-specific interactions in salience from combined feature contrasts: Evidence for a bottom-up saliency map in V1.<br \/>\nJournal of Vision, 7, 7, Article 6, 1-14.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Doya, K.<\/strong> (2007).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Reinforcement learning state estimator.<br \/>\nNeural Computation, 19, 3, 730-756.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Oztop, E.<\/strong> (2006).<\/span><br \/>\n<span style=\"font-family: helvetica;\">An upper bound on the minimum number of onomials required to separate dichotomies of {-1,1}n.<br \/>\nNeural Computation, 18, 3119-3138.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K.<\/strong> (2006).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning CPG-based biped locomotion with a policy gradient method.<br \/>\nRobotics and Autonomous Systems, 54,911-920.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><span style=\"font-family: helvetica;\"><strong>\u4e2d\u897f\u6df3, Schaal, S.<\/strong> (2006).<\/span><\/span><br \/>\n\u5e73\u621017\u5e74\u5ea6\u65e5\u672c\u795e\u7d4c\u56de\u8def\u5b66\u4f1a\u8ad6\u6587\u8cde\u53d7\u8cde\u5bfe\u8c61\u8ad6\u6587\u6982\u8981<br \/>\n\u795e\u7d4c\u56de\u8def\u5b66\u4f1a\u8a8c 2006\u5e743\u6708\u53f7 \u53d7\u8cde\u6982\u8981, 13, 1, 37-38.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Oztop, E., Imamizu, H., Cheng, G., Kawato, M.<\/strong> (2006).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A computational model of anterior intraparietal (AIP) neurons.<br \/>\nNeurocomputing, 69, 1354-1361.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Oztop, E., Kawato, M., Arbib, M. A.<\/strong> (2006).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Mirror neurons and limitation: A computationally guided review.<br \/>\nNeural Networks, 19 254-271.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Oztop, E., Franklin, D. W., Chaminade, T., Cheng, G.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Human-humanoid interaction: Is a humanoid robot perceived as a human?.<br \/>\nInternational Journal of Humanoid Robotics, 2, 4, 537-559.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Cheng, G., Schaal, S., Atkeson, C. G.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Editorial by guest editor.<br \/>\nInternational Journal of Humanoid Robotics, 2, 4, Editorial 389-390.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hale, J. G.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Practical and theoretical research into humanoid motion and interaction.<br \/>\nIEEE System, Man, and Cybernetics Society eNewsletter \/ Internet News Letter.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Hale, J. G., Pollick, F. E.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">&#8216;Sticky hands&#8217;: Learning and generalisation for cooperative physical interactions with a humanoid robot.<br \/>\nIEEE Transactions on Systems, Man, and Cybernetics : Part C, 35, 4, 512-521.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Arbib, M. A., Oztop, E., Zukow-goldring, P.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Language and the mirror system: A perception\/action based approach to communicative development.<br \/>\nCognitie, Creier, Comportament, IX, 3, 239-272.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Pollick, F. E., Hale, J. G., Tzoneva, M.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Perception of humanoid movement.<br \/>\nInternational Journal of Humanoid Robotics, 2, 3, 277-300.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Oztop, E., Kawato, M.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Conceptual and computational models of mirror neurons.<br \/>\nJournal of Japanese Neural Network Society, The Brain &amp; Neural Networks, 12, 1 61-73.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Gaskett, C., Ude, A., Cheng, G.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Hand-eye coordination through endpoint closed-loop and learned endpoint open-loop visual servo control.<br \/>\nInternational Journal of Humanoid Robotics, 2, 2, 203-224.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Morimoto, J., Doya, K.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Robust reinforcement learning.<br \/>\nNeural Computation, 17, 335-359.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Nakanishi, J., Farrell, J. A., Schaal, S.<\/strong> (2005).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Composite adaptive control with locally weighted statistical learning.<br \/>\nNeural Networks, 18, 71-90.<\/span><\/li>\n<\/ul>\n<ul>\n<li><strong>\u677e\u539f\u5d07\u5145, \u68ee\u672c\u6df3, \u4e2d\u897f\u6df3, \u4f50\u85e4\u96c5\u662d, \u9285\u8c37\u8ce2\u6cbb<\/strong> (2005).<br \/>\n\u65b9\u7b56\u3053\u3046\u914d\u6cd5\u3092\u7528\u3044\u305f\u52d5\u7684\u884c\u52d5\u5247\u306e\u7372\u5f97\uff1a2\u8db3\u6b69\u884c\u904b\u52d5\u3078\u306e\u9069\u7528<br \/>\n\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8c, J88-D-II, 1, 53-65.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Schaal, S., Sternad, D., Osu, R., Kawato, M.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Rhythmic arm movement is not discrete.<br \/>\nNature Neuroscience, 7, 10, 1136-1143.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Bentivegna, D. C., Atkeson, C. G., Cheng, G.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning tasks from observation and practice.<br \/>\nRobotics and Autonomous Systems, 47, 163-169.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., Kawato, M.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning from demonstration and adaptation of biped locomotion.<br \/>\nRobotics and Autonomous Systems, 47, 79-91.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Ude, A., Atkeson, C. G., Riley, M.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Programming full-body movements for humanoid robots by observation.<br \/>\nRobotics and Autonomous Systems, 47, 93-108.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Billard, A., Epars, Y., Calinon, S., Schaal, S., Cheng, G.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Discovering optimal imitation strategies.<br \/>\nRobotics and Autonomous Systems, 47-2004, 69-77.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Bentivegna, D. C., Atkeson, C. G., Ude, A., Cheng, G.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Learning to act from observation and practice.<br \/>\nInternational Journal of Humanoid Robotics, 1, 4, 585-611.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Pollard, N. S.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Closure and quality equivalence for efficient synthesis of grasps from examples.<br \/>\nInternational Journal of Robotics Research, 23, 6, 595-613.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Nakanishi, J., Schaal, S.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Feedback error learning and nonlinear learning adaptive control.<br \/>\nNeural Networks, 17, 1453-1465.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Miyamoto, H., Morimoto, J., Doya, K., Kawato, M.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">Reinforcement learning with via-point representation.<br \/>\nNeural Networks, 17, 299-305.<\/span><\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>\u4e2d\u897f\u6df3, Ijspeert, A., Schaal, S., Cheng, G.<\/strong> (2004).<\/span><br \/>\n\u904b\u52d5\u5b66\u7fd2\u30d7\u30ea\u30df\u30c6\u30a3\u30d6\u3092\u7528\u3044\u305f\u30ed\u30dc\u30c3\u30c8\u306e\u898b\u307e\u306d\u5b66\u7fd2<br \/>\n\u65e5\u672c\u30ed\u30dc\u30c3\u30c8\u5b66\u4f1a\u8a8c \u7279\u96c6\u300c\u30ed\u30dc\u30c3\u30c8\u306e\u904b\u52d5\u5b66\u7fd2\u300d, 22, 2, 165-170.<\/li>\n<\/ul>\n<ul>\n<li><span style=\"font-family: helvetica;\"><strong>Bentivegna, D. C., Atkeson, C. G., Cheng, G.<\/strong> (2004).<\/span><br \/>\n<span style=\"font-family: helvetica;\">A framework for learning from observation using primitives.<br \/>\nJournal of the Robotics Society of Japan, 22, 2, 176-181.<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Teramae, T., Matsubara, T., Noda, T., Morimoto, J. (2025\/03\/01). Optimizing non-assisted body part movements f [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":62,"menu_order":1,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2942","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/pages\/2942","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/comments?post=2942"}],"version-history":[{"count":2,"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/pages\/2942\/revisions"}],"predecessor-version":[{"id":2945,"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/pages\/2942\/revisions\/2945"}],"up":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/pages\/62"}],"wp:attachment":[{"href":"https:\/\/bicr.atr.jp\/bri\/wp-json\/wp\/v2\/media?parent=2942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}