Many anatomical factors, such as bone geometry and muscle condition, interact to affect human movements. This work aims to build a comprehensive musculoskeletal model and its control system that reproduces realistic human movements driven by muscle contraction dynamics. The variations in the anatomic model generate a spectrum of human movements ranging from typical to highly stylistic movements. To do so, we discuss scalable and reliable simulation of anatomical features, robust control of under-actuated dynamical systems based on deep reinforcement learning, and modeling of pose-dependent joint limits. The key technical contribution is a scalable, two-level imitation learning algorithm that can deal with a comprehensive full-body musculoskeletal model with 346 muscles. We demonstrate the predictive simulation of dynamic motor skills under anatomical conditions including bone deformity, muscle weakness, contracture, and the use of a prosthesis. We also simulate various pathological gaits and predictively visualize how orthopedic surgeries improve post-operative gaits.

Urban Regeneration, a program of land redevelopment in areas of moderate to high density urban land use, has complex procedure and consists of large sub-projects. This make a difficulty in understanding the overall work procedure and acquiring various information which are required to perform work. Especially, information and professional knowledge which are related to performing a work are needed to succeeding in Urban Regeneration. However, layman, such as landholder, resident, and unions of Urban Regeneration, lacks the professional knowledge and information. Therefore, this research aims to introduce IETM (Interactive Electronic Technical Manual) which is used in manufacturing and maintenance industry for providing procedures and guidelines for performing work to solve the problem.