Method for learning task skill and robot using thereof
AssigneeIucf-Hyu (Industry-University Cooperation Foundation Hanyang University)
InventorsIl-Hong Suh, Sang-Hyoung Lee, Jae Pyung Hwang
Life of this patent
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Abstract
Provided are a method for learning task skill and a robot using the same. The modeling method for learning a task skill includes: receiving training data for a task to be performed by a learning engine; dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model; and learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment.
Claims13
A modeling method for learning a task skill, which is performed by a learning engine having at least one processor, the modeling method comprising:
- receiving training data for a task to be performed by the learning engine;
- dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model;
- and
- learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment;
- wherein said dividing of the received training data into segments includes:
- determining a segmentation point by analyzing geometric properties of a Gaussian mixture model (GMM) and a Gaussian mixture regression (GMR) for the received training data;
- and
- dividing the training data into segments based on the determined segmentation point.
A modeling method for learning a task skill, which is performed by a learning engine having at least one processor, the modeling method comprising:
- receiving training data for a task to be performed by the learning engine;
- dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model;
- and
- learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment;
- wherein said learning of a basis skill is performed by generating a hidden Markov model (HMM) by using the divided segment.
A modeling method for learning a task skill, which is performed by a learning engine having at least one processor, the modeling method comprising:
- receiving training data for a task to be performed by the learning engine;
- dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model;
- learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment;
- receiving new training data for a task to be performed by the learning engine;
- generating, by the learning engine, a classifier having a threshold model for classifying the basis skill and the new training data into segments;
- and
- modeling, by the learning engine, a task skill of multi paths expanded from the basis skill by combining the learned basis skills with the new training data classified into segments by using the generated classifier.
Dependencies are inferred from the claim text.