What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
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.
His most cited work include:
- Learning from demonstration and adaptation of biped locomotion (345 citations)
- Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives (244 citations)
- CB: A Humanoid Research Platform for Exploring NeuroScience (185 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Robot (41.58%)
- Artificial intelligence (39.60%)
- Control theory (30.69%)
What were the highlights of his more recent work (between 2015-2021)?
- Robot (41.58%)
- Artificial intelligence (39.60%)
- Exoskeleton (13.37%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- A small number of abnormal brain connections predicts adult autism spectrum disorder (165 citations)
- Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation. (78 citations)
- Variable Ankle Stiffness Improves Balance Control: Experiments on a Bipedal Exoskeleton (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
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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