ACM Computing Surveys (CSUR)
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
International Journal of Robotics Research
IEEE Transactions on Robotics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Natural motion animation through constraining and deconstraining at will
IEEE Transactions on Visualization and Computer Graphics
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Conceptual Imitation Learning in a Human-Robot Interaction Paradigm
ACM Transactions on Intelligent Systems and Technology (TIST)
Robot learning from demonstration by constructing skill trees
International Journal of Robotics Research
International Journal of Robotics Research
On-line motion synthesis and adaptation using a trajectory database
Robotics and Autonomous Systems
Skill learning and inference framework
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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This paper describes a novel approach for incremental learning of human motion pattern primitives through online observation of human motion. The observed time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then abstracted into a stochastic model representation and automatically clustered and organized. As new motion patterns are observed, they are incrementally grouped together into a tree structure, based on their relative distance in the model space. The tree leaves, which represent the most specialized learned motion primitives, are then passed back to the segmentation algorithm so that as the number of known motion primitives increases, the accuracy of the segmentation can also be improved. The combined algorithm is tested on a sequence of continuous human motion data that are obtained through motion capture, and demonstrates the performance of the proposed approach.