Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Imitation: a means to enhance learning of a synthetic protolanguage in autonomous robots
Imitation in animals and artifacts
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Learning to walk through imitation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Study on similarity imitation constraints of biped walking for humanoid robot
International Journal of Computing Science and Mathematics
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Programming a humanoid robot to perform an action that takes the robot's complex dynamics into account is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot's dynamics and environment in order to devise complex control algorithms for generating a stable dynamic motion. Training using human motion capture is an intuitive and flexible approach to programming a robot but directly applying motion capture data to a robot usually results in dynamically unstable motion. Optimization using high-dimensional motion capture data in the humanoid full-body joint-space is also typically intractable. In previous work, we proposed an approach that uses dimensionality reduction to achieve tractable imitation-based learning in humanoids without the need for a physics-based dynamics model. This work was based on a 3-D "eigenpose" representation. However, for some motion patterns, using only three dimensions for eigenposes is insufficient. In this paper, we propose a new method for motion optimization based on high-dimensional eigenpose data. A one-dimensional computationally efficient motion-phase optimization method is implemented along with a newly developed cylindrical coordinate transformation technique for hyperdimensional subspaces. This results in a fast learning algorithm and very accurate motion imitation. We demonstrate the new algorithm on a Fujitsu HOAP-2 humanoid robot model in a dynamic simulator and show that a dynamically stable sidestep motion can be successfully learned by imitating a human demonstrator.