Human motor system executes the motor command from high-order regions, rigorously controls the musculoskeletal system and learns to adapt to novel stimuli of dynamic environments. Due to the highly redundant characteristics of the motor system, understanding the optimal and individualized principles of motor control and motor learning is still a scientific challenge. Robots interacting with or mimicking human motor systems through a completely controlled manner provide a novel perspective for studying the human motor system. On one hand, wearable robots, such as exoskeletons, prostheses and artificial limbs, closely interact with human, alter biomechanical, moto-neuronal, motor cortical and subjective preference aspects, thus fostering new homeostasis of human motor system. The alternation and adaptation of motor systems under such human-robot interactions lead to a well-controlled treatment and can offer novel viewing angles. On the other hand, biomimetic and bio-inspired robots that mimic human motor control or motor learning, from the perspectives of musculoskeletal actuation, exploration and exploitation-balanced motor decision and neuromechanical control of motions, reflect the inner principles of human motor system through comparison studies.
Despite the advancement of robotics greatly enabling the potential of such studies, research under this viewpoint is still rare. The topics like inner beliefs, neuromechanical principles and cognitive aspects of motor learning are still left to be understood. The complexity of the human motor system and the rapidly developed robotic technologies and their combined promising usages in neuroscience, diagnostics, rehabilitation and augmentation highlight the necessity of our focus on bridging the gap between robotics and the human motor system study. The Research Topic aims to summarize the recent development of the robot-assisted studies of human motor systems and the motor principle-inspired robotic design.
We welcome submissions in the form of original research, systematic reviews, method articles, and perspective articles. Areas of focus include but are not limited to:
• Biomechanical adaptation under human-robot interactions
• Simulation of neuromechanical control
• Computational modelling
• Bio-inspired design of robots
• Motor control and motor learning
• Exploration and exploitation that follow motor decision principles
• Reinforcement learning-based modeling
• Cognitive and cortical alternation under human-robot interactions
• Moto-neuronal aspects of wearable robots
Human motor system executes the motor command from high-order regions, rigorously controls the musculoskeletal system and learns to adapt to novel stimuli of dynamic environments. Due to the highly redundant characteristics of the motor system, understanding the optimal and individualized principles of motor control and motor learning is still a scientific challenge. Robots interacting with or mimicking human motor systems through a completely controlled manner provide a novel perspective for studying the human motor system. On one hand, wearable robots, such as exoskeletons, prostheses and artificial limbs, closely interact with human, alter biomechanical, moto-neuronal, motor cortical and subjective preference aspects, thus fostering new homeostasis of human motor system. The alternation and adaptation of motor systems under such human-robot interactions lead to a well-controlled treatment and can offer novel viewing angles. On the other hand, biomimetic and bio-inspired robots that mimic human motor control or motor learning, from the perspectives of musculoskeletal actuation, exploration and exploitation-balanced motor decision and neuromechanical control of motions, reflect the inner principles of human motor system through comparison studies.
Despite the advancement of robotics greatly enabling the potential of such studies, research under this viewpoint is still rare. The topics like inner beliefs, neuromechanical principles and cognitive aspects of motor learning are still left to be understood. The complexity of the human motor system and the rapidly developed robotic technologies and their combined promising usages in neuroscience, diagnostics, rehabilitation and augmentation highlight the necessity of our focus on bridging the gap between robotics and the human motor system study. The Research Topic aims to summarize the recent development of the robot-assisted studies of human motor systems and the motor principle-inspired robotic design.
We welcome submissions in the form of original research, systematic reviews, method articles, and perspective articles. Areas of focus include but are not limited to:
• Biomechanical adaptation under human-robot interactions
• Simulation of neuromechanical control
• Computational modelling
• Bio-inspired design of robots
• Motor control and motor learning
• Exploration and exploitation that follow motor decision principles
• Reinforcement learning-based modeling
• Cognitive and cortical alternation under human-robot interactions
• Moto-neuronal aspects of wearable robots
