His main research concerns Robot, Control theory, Artificial intelligence, Humanoid robot and Reinforcement learning. His Robot research is multidisciplinary, relying on both Simulation, Robustness and Adaptation. His work on Control theory and Torque as part of general Control theory research is frequently linked to Poincaré map and Simple, bridging the gap between disciplines.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision. He focuses mostly in the field of Humanoid robot, narrowing it down to matters related to Control engineering and, in some cases, Robot kinematics and Robot locomotion. His studies in Reinforcement learning integrate themes in fields like Gradient method and Bellman equation.
Jun Morimoto spends much of his time researching Robot, Artificial intelligence, Control theory, Humanoid robot and Reinforcement learning. His Robot research incorporates elements of Control engineering and Simulation, Exoskeleton. In his research, Robotics is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
In the subject of general Control theory, his work in Control theory and Optimal control is often linked to Central pattern generator, thereby combining diverse domains of study. His Humanoid robot study combines topics from a wide range of disciplines, such as Degrees of freedom, Human–computer interaction, Motor learning, Motion control and Nonlinear system. His biological study spans a wide range of topics, including Mathematical optimization, Gradient method, Bellman equation, State space and Trajectory.
The scientist’s investigation covers issues in Robot, Artificial intelligence, Exoskeleton, Control theory and Control engineering. His work on Robot deals in particular with Humanoid robot and Robot control. His Artificial intelligence study incorporates themes from Computer vision and Pattern recognition.
Many of his research projects under Control theory are closely connected to Control with Control, tying the diverse disciplines of science together. His work deals with themes such as Simulation and Artificial muscle, which intersect with Torque. His Reinforcement learning research is multidisciplinary, incorporating elements of Robot learning, Adaptation and Dimensionality reduction.
His primary areas of investigation include Robot, Artificial intelligence, Electromyography, Exoskeleton and Computer vision. His work on Human–robot interaction as part of general Robot study is frequently linked to Movement, therefore connecting diverse disciplines of science. His study in Robot control and Reinforcement learning is done as part of Artificial intelligence.
He interconnects Robot learning and Inverted pendulum in the investigation of issues within Robot control. He usually deals with Electromyography and limits it to topics linked to Torque and Simulation. In his study, Control engineering is strongly linked to Control theory, which falls under the umbrella field of Exoskeleton.
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Learning from demonstration and adaptation of biped locomotion
Jun Nakanishi;Jun Morimoto;Gen Endo;Gordon Cheng.
Robotics and Autonomous Systems (2004)
Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives
Aleš Ude;Andrej Gams;Tamim Asfour;Jun Morimoto.
IEEE Transactions on Robotics (2010)
Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning
Jun Morimoto;Kenji Doya.
Robotics and Autonomous Systems (2001)
CB: A Humanoid Research Platform for Exploring NeuroScience
G. Cheng;Sang-Ho Hyon;J. Morimoto;A. Ude.
ieee-ras international conference on humanoid robots (2006)
Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot
Gen Endo;Jun Morimoto;Takamitsu Matsubara;Jun Nakanishi.
The International Journal of Robotics Research (2008)
Robust Reinforcement Learning
Jun Morimoto;Kenji Doya.
neural information processing systems (2000)
A small number of abnormal brain connections predicts adult autism spectrum disorder
Noriaki Yahata;Jun Morimoto;Ryuichiro Hashimoto;Giuseppe Lisi.
Nature Communications (2016)
ROBOT APPARATUS AND A METHOD FOR CONTROLLING THE POSTURE OF A ROBOT FOR STABILIZING THE POSTURE OF THE ROBOT ACCORDING TO PERIODICAL MOTION
Cheng Gordon;Endo Gen;Kawato Mitsuo;Morimoto Jun.
Experimental Studies of a Neural Oscillator for Biped Locomotion with QRIO
Gen Endo;Jun Nakanishi;Jun Morimoto;G. Cheng.
international conference on robotics and automation (2005)
Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface
Takamitsu Matsubara;Jun Morimoto.
IEEE Transactions on Biomedical Engineering (2013)
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