Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3306346.3322972 | ACM Transactions on Graphics (TOG) |
Keywords | Field | DocType |
anatomical human modeling, deep reinforcement learning, gait analysis, joint range of motion modeling, locomotion control, musculoskeletal modeling | Contracture,Computer vision,Gait,Motor skill,Simulation,Gait analysis,Dynamical systems theory,Artificial intelligence,Control system,Robust control,Mathematics,Reinforcement learning | Journal |
Volume | Issue | ISSN |
38 | 4 | 0730-0301 |
Citations | PageRank | References |
5 | 0.42 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-Hwan Lee | 1 | 77 | 18.94 |
| Moon Seok Park | 2 | 34 | 2.51 |
| Kyoung-Min Lee | 3 | 5 | 1.43 |
| Jehee Lee | 4 | 1912 | 118.33 |