The Research Topic will include extended manuscripts based on submissions to the Human Movement Understanding for Intelligent Robots and Systems workshop at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), as well as relevant non-workshop submissions.
Robotics research has drawn significant inspiration from humans as a system: in the design of the anthropomorphic aspects of manipulators, sensors, and actuators, approaches for coordinating full-body motions, and the higher level strategies for realizing complex tasks and interacting with the external environment and other humans.
Today, robotics as a field has matured to the point where methodologies developed and used in robotics may be leveraged to address research questions in many other fields, ranging from neuroscience to computer animation. In recent years, robotic computational strategies have contributed significantly to the analysis of human motion and manipulation skills. These analyses have led to advancements in the field of robotics, enabling human-inspired capabilities in robots and simulated systems as well as robot learning through observation. Furthermore, they also allowed for a deeper understanding of the human body and its motion generation strategies. This requires accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics, and actuation, and suitable criteria for the characterization of performance. Building on methodologies and techniques developed in robotics, a host of new, effective tools have been established for the synthesis of human motion. These developments are providing new avenues for exploring human motion ¬ with exciting prospects for novel clinical therapies, athletic training, performance improvement, and worker's ergonomic evaluation.
In recent years, not only a deeper understanding of the human body and its motion generation strategy, but also robot technology that physically improves human movement has been actively developed, and it is expected to be used in various scenes. There is a need to realize technology that assists people's daily lives and technology that physically assists people's movements. For that purpose, it is important to develop a technology in which a robot estimates and predicts a person's movement and cooperates with the person's movement intention. On the other hand, the current robot technology is effective for analyzing general movements with a clear causal relationship, but it is considered not to be effective for individually responding to various human movements. We are focusing on AI-based human motion estimation and prediction technology as a technology that can respond to a wide variety of human motions and the diversity of individual motions. By capturing time-series data of human movements in advance and learning by machine learning and deep learning, we believe that robots will be able to respond to the diversity of human movements.
We welcome submissions on topics including, but not limited to, the following:
• Robotics
• Kinesiology
• Machine Learning
• Biomechanics
• Physical Therapy
• Computer Graphics
• Virtual Reality
• Assistive Technologies
• Rehabilitation Robotics
The Research Topic will include extended manuscripts based on submissions to the Human Movement Understanding for Intelligent Robots and Systems workshop at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), as well as relevant non-workshop submissions.
Robotics research has drawn significant inspiration from humans as a system: in the design of the anthropomorphic aspects of manipulators, sensors, and actuators, approaches for coordinating full-body motions, and the higher level strategies for realizing complex tasks and interacting with the external environment and other humans.
Today, robotics as a field has matured to the point where methodologies developed and used in robotics may be leveraged to address research questions in many other fields, ranging from neuroscience to computer animation. In recent years, robotic computational strategies have contributed significantly to the analysis of human motion and manipulation skills. These analyses have led to advancements in the field of robotics, enabling human-inspired capabilities in robots and simulated systems as well as robot learning through observation. Furthermore, they also allowed for a deeper understanding of the human body and its motion generation strategies. This requires accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics, and actuation, and suitable criteria for the characterization of performance. Building on methodologies and techniques developed in robotics, a host of new, effective tools have been established for the synthesis of human motion. These developments are providing new avenues for exploring human motion ¬ with exciting prospects for novel clinical therapies, athletic training, performance improvement, and worker's ergonomic evaluation.
In recent years, not only a deeper understanding of the human body and its motion generation strategy, but also robot technology that physically improves human movement has been actively developed, and it is expected to be used in various scenes. There is a need to realize technology that assists people's daily lives and technology that physically assists people's movements. For that purpose, it is important to develop a technology in which a robot estimates and predicts a person's movement and cooperates with the person's movement intention. On the other hand, the current robot technology is effective for analyzing general movements with a clear causal relationship, but it is considered not to be effective for individually responding to various human movements. We are focusing on AI-based human motion estimation and prediction technology as a technology that can respond to a wide variety of human motions and the diversity of individual motions. By capturing time-series data of human movements in advance and learning by machine learning and deep learning, we believe that robots will be able to respond to the diversity of human movements.
We welcome submissions on topics including, but not limited to, the following:
• Robotics
• Kinesiology
• Machine Learning
• Biomechanics
• Physical Therapy
• Computer Graphics
• Virtual Reality
• Assistive Technologies
• Rehabilitation Robotics
