SYSTEMS AND METHODS FOR FEW-SHOT TRANSFER LEARNING
First Claim
1. A method for training a controller to control a robotic system in a target domain, the method comprising:
- receiving a neural network of an original controller for controlling the robotic system based on a plurality of origin data samples from an origin domain and corresponding labels in a label space the neural network of the original controller comprising a plurality of encoder parameters and a plurality of classifier parameters, the neural network being trained to;
map an input data sample from the origin domain to a feature vector in a feature space in accordance with the encoder parameters; and
assign a label of the label space to the input data sample based on the feature vector in accordance with the classifier parameters;
updating the encoder parameters to minimize a dissimilarity, in the feature space, between;
a plurality of origin feature vectors computed from the origin data samples; and
a plurality of target feature vectors computed from a plurality of target data samples from the target domain, the target data samples having a smaller cardinality than the origin data samples; and
updating the controller with the updated encoder parameters to control the robotic system in the target domain.
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Abstract
A method for training a controller to control a robotic system includes: receiving a neural network of an original controller for the robotic system based on origin data samples from an origin domain and labels in a label space, the neural network including encoder and classifier parameters, the neural network being trained to: map an input data sample from the origin domain to a feature vector in a feature space using the encoder parameters; and assign a label of the label space to the input data sample using the feature vector based on the classifier parameters; updating the encoder parameters to minimize a dissimilarity, in the feature space, between: origin feature vectors computed from the origin data samples; and target feature vectors computed from target data samples from a target domain; and updating the controller with the updated encoder parameters to control the robotic system in the target domain.
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Citations
27 Claims
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1. A method for training a controller to control a robotic system in a target domain, the method comprising:
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receiving a neural network of an original controller for controlling the robotic system based on a plurality of origin data samples from an origin domain and corresponding labels in a label space the neural network of the original controller comprising a plurality of encoder parameters and a plurality of classifier parameters, the neural network being trained to; map an input data sample from the origin domain to a feature vector in a feature space in accordance with the encoder parameters; and assign a label of the label space to the input data sample based on the feature vector in accordance with the classifier parameters; updating the encoder parameters to minimize a dissimilarity, in the feature space, between; a plurality of origin feature vectors computed from the origin data samples; and a plurality of target feature vectors computed from a plurality of target data samples from the target domain, the target data samples having a smaller cardinality than the origin data samples; and updating the controller with the updated encoder parameters to control the robotic system in the target domain. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system for training a controller to control a robotic system in a target domain, the system comprising:
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a processor; and non-volatile memory storing instructions that, when executed by the processor, cause the processor to; receive a neural network of an original controller for controlling the robotic system based on a plurality of origin data samples from an origin domain and corresponding labels in a label space, the neural network of the original controller comprising a plurality of encoder parameters and a plurality of classifier parameters, the neural network being trained to; map an input data sample from the origin domain to a feature vector in a feature space in accordance with the encoder parameters; and assign a label of the label space to the input data sample based on the feature vector in accordance with the classifier parameters; update the encoder parameters to minimize a dissimilarity between; a plurality of origin feature vectors computed from the origin data samples; and a plurality of target feature vectors computed from a plurality of target data samples from the target domain, the target data samples having a smaller cardinality than the origin data samples; and update the controller with the updated encoder parameters to control the robotic system in the target domain. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to:
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receive a neural network of an original controller for controlling a robotic system based on a plurality of origin data samples from an origin domain and corresponding labels in a label space, the neural network of the original controller comprising a plurality of encoder parameters and a plurality of classifier parameters, the neural network being trained to; map an input data sample from the origin domain to a feature vector in a feature space in accordance with the encoder parameters; and assign a label of the label space to the input data sample based on the feature vector in accordance with the classifier parameters; update the encoder parameters to minimize a dissimilarity between; a plurality of origin feature vectors computed from the origin data samples; and a plurality of target feature vectors computed from a plurality of target data samples from a target domain, the target data samples having a smaller cardinality than the origin data samples; and update the controller with the updated encoder parameters to control a robotic system in the target domain. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27)
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Specification