Trainable convolutional network apparatus and methods for operating a robotic vehicle
First Claim
1. A method of operating a robotic device by a computerized neuron network comprising an input layer, an intermediate layer and an output layer of neurons, the method comprising:
- during one operation of a plurality of operations;
causing the robotic device to execute an action along a first trajectory in accordance with a first control signal determined based on a sensory input;
determining, by the output layer, a performance measure based on an evaluation of the first trajectory and indication related to a target trajectory provided by a trainer;
conveying information related to the performance measure to the input layer; and
updating one or more learning parameters of the input layer in accordance with the information; and
during a subsequent operation of a plurality of operations;
causing the robotic device to execute the action along a second trajectory in accordance with a second control signal determined based on the sensory input;
wherein;
the execution of the action along the second trajectory is characterized by a second performance measure; and
the updating is configured to displace the second trajectory closer towards the target trajectory relative to the first trajectory.
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Abstract
A robotic vehicle may be operated by a learning controller comprising a trainable convolutional network configured to determine control signal based on sensory input. An input network layer may be configured to transfer sensory input into a hidden layer data using a filter convolution operation. Input layer may be configured to transfer sensory input into hidden layer data using a filter convolution. Output layer may convert hidden layer data to a predicted output using data segmentation and a fully connected array of efficacies. During training, efficacy of network connections may be adapted using a measure determined based on a target output provided by a trainer and an output predicted by the network. A combination of the predicted and the target output may be provided to the vehicle to execute a task. The network adaptation may be configured using an error back propagation method. The network may comprise an input reconstruction.
343 Citations
22 Claims
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1. A method of operating a robotic device by a computerized neuron network comprising an input layer, an intermediate layer and an output layer of neurons, the method comprising:
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during one operation of a plurality of operations; causing the robotic device to execute an action along a first trajectory in accordance with a first control signal determined based on a sensory input; determining, by the output layer, a performance measure based on an evaluation of the first trajectory and indication related to a target trajectory provided by a trainer; conveying information related to the performance measure to the input layer; and updating one or more learning parameters of the input layer in accordance with the information; and during a subsequent operation of a plurality of operations; causing the robotic device to execute the action along a second trajectory in accordance with a second control signal determined based on the sensory input; wherein; the execution of the action along the second trajectory is characterized by a second performance measure; and the updating is configured to displace the second trajectory closer towards the target trajectory relative to the first trajectory. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of generating a predicted control output by an adaptive controller of a robotic apparatus comprising a predictor and a combiner, the method comprising:
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configuring the adaptive controller apparatus to detect an object in sensory input provided by a sensor of the robotic apparatus, the object detection causing generation of a control output based on a characteristic of the object; configuring the predictor to determine a predicted control output based on the characteristic of the object; configuring the combiner to determine a combined output based on a control input and the predicted control output, the combined output being characterized by a transform function; determining a performance measure based on the predicted control output and the combined output; updating one or more learning parameters of the adaptive controller in accordance with the performance measure; and configuring the adaptive controller to provide the combined output to the robotic apparatus, the combined output configured to cause the robotic apparatus to execute a maneuver in accordance with the characteristic of the object. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A method of operating a robotic device using a computerized neuron network having a plurality of layers of neurons, the method comprising:
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causing the robotic device to execute an action along a first trajectory in accordance with a first control signal, the first signal determined based at least on a sensory input; determining a performance measure based on an evaluation of the first trajectory relative to a target trajectory; updating one or more learning parameters of a first of the plurality of layers in accordance with information relating to the determined performance measure; and causing the robotic device to execute the action along a second trajectory in accordance with a second control signal, the second signal determined based at least on a sensory input and the updated one or more learning parameters, the second trajectory being closer to the target trajectory than the first trajectory.
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16. A computerized neuron network apparatus configured to provide a response based on analysis of visual input frames, the computerized neuron network apparatus comprising:
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an input component comprising first portion of neurons configured to implement a convolutional operation on the visual input frames using a plurality of filter masks, the operation being configured to produce convolved input frames; an output component comprising at least one output neuron configured to provide an output based on the at least one output neuron reaching a target state; a connection component configured to couple the input component to the at least one output neuron via an efficacy array; and a cost estimation component configured to determine a first similarity measure between a given response and a target response; wherein; the given response is configured based on the output; the first similarity measure determined based on a first analysis of a first frame of the visual input frames is configured to cause an update of the neuron network, the update of the neuron network being configured to increase a second similarity measure determined based on a second analysis of a second frame of the visual input frames subsequent to the first frame; the convolved input frames are configured to enable detection of an object; the output is configured based on the detected object; and the given response is configured to be provided to the computerized neuron network apparatus, the given response being configured to cause the computerized neuron network apparatus to execute a first action in accordance with the detected object. - View Dependent Claims (17, 18, 19, 20, 21, 22)
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Specification