2019-2020 Assessment of the Army Research Laboratory (2021) / Chapter Skim
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2 Network and Information Sciences
2 Network and Information Sciences
Pages 15-26
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From page 15... ...
Research in the area of networks and cyber includes projects that fall into the general area of humanrobot/machine interactions, with the two principal threads being scene narrative generation for humans by robots and robot learning from human demonstrations. This body of work is ambitious and has the potential to disrupt the way human-robot interactions are considered for future battlefields.
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From page 16... ...
The research review consisted of a number of presentations on topics such as learning from imitation, risk-aware learning, HDR saliency, and reinforcement learning for adaptable agents. There were also a number of poster presentations in these subject areas, including research on imitation from observation, semantic-based autonomous navigation, learned control policy, and information agents for value assessment.
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From page 17... ...
Such an approach can contribute to improved situational understanding. Virtual reality simulations were used to generate video data with motion parallax to train deep learning neural networks that will be tested against spatiotemporal test data.
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From page 18... ...
Although some interesting preliminary results have been generated, it would be important to clearly enunciate the rationale for the chosen approach, what competing strategies are possible, and whether the proposed approach performs better than existing solutions to this problem. Neural Network Models for Low-Resource and Morphologically Complex Language Processing This project is designed to improve the information extraction capabilities for morphologically complex languages such as Russian and Ukrainian by developing deep neural network-based morphological classifiers for such languages.
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From page 19... ...
The project on augmented reality for human-robot teaming represents a promising area for ARL researchers; partnering with the virtual reality research team may offer new avenues for exploration in this context. In particular, research on human-robot teaming could benefit from collaboration with the ongoing work on immersive virtual reality.
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From page 20... ...
While not the focus of the ongoing work, this research could potentially also save communications bandwidth by the transmittal of the textual descriptions of the scenes instead of the full motion video. Low-Power, Low-Frequency Mobile Networking The goals and objectives of the project were to understand diverse communications modalities for more robust and covert operations, understand physical layer challenges and limitations, improve low probability of detection and low probability of intercept, and exploit autonomous agents that enhance networking capabilities and control radio radiation signatures.
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From page 21... ...
The validation of the approach with real vehicles lends additional credence to the results. Fog Computing and the Tactical Distributed Ledger These research projects represent two approaches to ensuring robust operation of a "network of things" such as cameras, directories, storage nodes, and so on, where the individual things fail, are replaced, or have degraded performance.
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From page 22... ...
The tactical distributed ledger stores state in multiple locations, and "smart contract" technology allows resources to advertise their application programming interfaces and permits nodes that require services from resources to be able to advertise their needs. The fog computing project is at an early stage of framing the overall concept.
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From page 23... ...
Several research projects fall into the general area of human-robot/machine interactions, with the two principal threads: scene narrative generation for humans by robots and robot learning from human demonstrations. This body of work is ambitious and has the potential to disrupt the way human-robot interactions are considered for future battlefields.
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From page 24... ...
A stronger focus in areas like adversarial learning, integration of simulation in ML, and security related to ML would have provided a better understanding of the scope of ongoing work. As an example, simulation is emerging as a prominent element in training AI/ML systems, where physics-based simulation engines provide data to train autonomous vehicles and robots.
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From page 25... ...
In the area of human-robot interactions, advances in narrative generation are significant and have the potential of not only reducing soldier workload but also reducing the bandwidth requirement for tactical networks. The work related to active defense was considered to be exceptionally strong and provides a reduction in the cyber vulnerability of Army vehicles and other cyber-enabled systems.
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From page 26... ...
In the absence of analytical solutions, such simulation tools would provide valuable insight into the understanding of network dynamics in complex environments. ARL should consider additional expertise and access to computational resources to facilitate this enhancement.
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