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- W1494695454 abstract "Programming a humanoid robot to perform an action that takes into account the robot’s complex dynamics is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot’s dynamics and the environment in order to devise complex control algorithms for generating a stable dynamic motion. Training using human motion capture (mocap) data is an intuitive and flexible approach to programming a robot but direct usage of kinematic data from mocap usually results in dynamically unstable motion. Furthermore, optimization using mocap data in the high-dimensional full-body joint-space of a humanoid is typically intractable. In this chapter, we purposes a new model-free approach to tractable imitation-based learning in humanoids by using eigenposes." @default.
- W1494695454 created "2016-06-24" @default.
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- W1494695454 date "2010-01-01" @default.
- W1494695454 modified "2023-09-26" @default.
- W1494695454 title "Learning to Imitate Human Actions through Eigenposes" @default.
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- W1494695454 doi "https://doi.org/10.1007/978-3-642-05181-4_15" @default.
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