Hrl Laboratories, LLC

Steven W Skorheim

Praveen K Pilly

Tiffany Hwu

David J Huber

Nigel D Stepp

Jaehoon Choe

Michael D Howard

Qin Jiang

Vincent De Sapio

Youngkwan Cho

Ruggero Scorcioni

Jose Cruz-Albrecht

Nicholas A Ketz

Myana Anderson

Described is a system for improving machine operation performance. The system assigns and displays, on an interface having multiple interactive controls, a performance score for each skill of a sequential task in a simulation of operation of a machine. Based on the performance scores, one or more skills to improve with targeted training are identified and displayed on the interface. A training scenario of skills to perform via the interactive controls in a subsequent simulation is recommended to improve the performance scores. Following performance of the training scenario in the subsequent simulation, the system assigns and displays, on the interface, a new performance score for each skill performed. The training scenario is adapted based on the new performance scores.

Human machine teaching system for sequential task training

A pilot support system includes: a processing circuit; and memory storing instructions that, when executed by the processing circuit, cause the processing circuit to: receive input data regarding a current state of a vehicle; encode the input data to generate encoded input data; supply the encoded input data to a trained statistical model; compute a current context of the vehicle based on the input data using the trained statistical model; compute one or more pilot feedback indicators based on the current context using the trained statistical model; and provide the one or more pilot feedback indicators to a cockpit of the vehicle.

Neural network-based system for flight condition analysis and communication

Described is a system for human-machine teaching for vehicle operation. The system determines currently enabled status reporting modes on a vehicle interface of a vehicle. The currently enabled status reporting modes are compared to a set of preferred status reporting modes of previous users. Based on the comparison, a status reporting mode is selected. A current operational status of the vehicle is reported to a current user, via the vehicle interface, using the selected status reporting mode. The system then determines preferred solutions of previous users to address the current operational status of the vehicle. Suggestions to address the current operational status of the vehicle based on the preferred solutions are reported to the user via the vehicle interface. A vehicle action corresponding to a solution selected by the current user is implemented via a vehicle component.

Bidirectional machine teaching interface for human-machine co-pilots

Described is a system for multimodal machine-aided comprehension analysis. The system can be implemented in an augmented reality headset that, in conjunction with a processor, generates an initial scene graph of a scene proximate the user. Items and labels are presented, with the headset tracking eye movements of the user as the user gazes upon the subject labels, item labels, and relationship labels. A resulting scene graph (having relationship triplets) is generated based on the eye movements of the user and an amount of time the user spends gazing upon each of the display components. A comprehension model is generated by estimating a user's comprehension of the relationship triplets, with a knowledge model being generated based on a known knowledge graph and the comprehension model. Cues are then presented to the user based on the comprehension and knowledge models to assist the user in their comprehension of the scene.

System for multimodal machine-aided comprehension analysis and assistance

A method of training an artificial neural network having a series of layers and at least one weight matrix encoding connection weights between neurons in successive layers. The method includes receiving, at an input layer of the series of layers, at least one input, generating, at an output layer of the series of layers, at least one output based on the at least one input, generating a reward based on a comparison of between the at least one output and a desired output, and modifying the connection weights based on the reward. Modifying the connection weights includes maintaining a sum of synaptic input weights to each neuron to be substantially constant and maintaining a sum of synaptic output weights from each neuron to be substantially constant.

Artificial neural networks having competitive reward modulated spike time dependent plasticity and methods of training the same

Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

Method of real time vehicle recognition with neuromorphic computing network for autonomous driving

A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.

System and method for synthetic aperture radar target recognition using multi-layer, recurrent spiking neuromorphic networks

Described is a system for adaptable neurostimulation intervention. The system monitors a set of neurophysiological signals in real-time and updates a physiological and behavioral model. The set of neurophysiological signals are classified in real-time based on the physiological and behavioral model. A neurostimulation intervention schedule is generated based on the classified set of neurophysiological signals. The system activates electrodes via a neurostimulation intervention system to cause a timed neurostimulation intervention to be administered based on the neurostimulation intervention schedule. The neurostimulation intervention schedule and timed neurostimulation intervention are refined based on new sets of neurophysiological signals.

Method for automated closed-loop neurostimulation for improving sleep quality

Described is a system for transcranial stimulation to improve cognitive function. During operation, the system generates a customized stimulation pattern based on damaged white matter. Further, data is obtained representing natural brain oscillations of a subject. Finally, while the subject is awake, one or more electrodes are activated in phase with the natural brain oscillations and based on the customized stimulation pattern.

Transcranial stimulation system and method to improve cognitive function after traumatic brain injury

Described is a closed-loop intervention control system for memory consolidation in a subject. During operation, the system simulates memory changes of a first memory in a subject during waking encoding of the memory, and then while the subject is sleeping and coupled to an intervention system. Based on the simulated memory changes, the system predicts behavioral performance for the first memory, the behavioral performance being a probability that the first memory can be recalled on cue. The system can be used to control operation (e.g., turn on or off) of the intervention system with respect to the first memory based on the behavioral performance of the first memory determined by the simulation.

Closed-loop intervention control system

Described is a system for memory improvement intervention. Based on both real-time EEG data and a neural model, the system simulates replay of a person's specific memory during a sleep state. Using the neural model, a prediction of behavioral performance of the replay of the specific memory is generated. If the prediction is below a first threshold, then using a memory enhancement intervention system, the system applies an intervention during the sleep state to improve consolidation of the specific memory. If the prediction is below a second threshold, the system reduces the intervention performed using the memory enhancement intervention system.

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Canoga Park, CA, United States of America

Steven W Skorheim