Related News from around CCDC ARL
Army researchers developed a novel computational model for gathering cognitive data that may be a game changer in the fields of neuroscience and econometrics, and has broad relevance to networked and multi-agent systems.
At the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and the University of Maryland, College Park, researchers developed what is known as the Autoregressive Linear Mixture, or ALM, a novel model for analyzing time-series data, or how things change over time.
Future technology may be able to monitor and modify the brain to produce enhanced team performance, while increasing the efficiency and accuracy of decisions. The U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, the Italian Institute of Technology, Italy, Harvard Medical Schooland the University of California, Irvine teamed up to study and advance research on the complexities of the human brain. Scientific Reports recently published the discoveries from their study.
Army researchers recently completed a simulation study where crew members and artificial intelligent agents demonstrated trust and cohesion while working together.
U.S. Army Combat Capabilities Development Command’s Army Research Laboratory researchers and U.S. Army Military Academy cadets conducted the study as part of an academic capstone project. It also supports the Army Wingman Joint Capabilities Technology Demonstration and the Army’s Next Generation Combat Vehicle mission prioritization.
Something is different, and you can’t quite put your finger on it, but your robot can. Even small changes in your surroundings could indicate danger. Imagine a robot could detect those changes, and a warning could immediately alert you through a display in your eyeglasses. That is what U.S. Army scientists are developing with sensors, robots, real-time change detection and augmented reality wearables.
Army researchers demonstrated in a real-world environment the first human-robot team in which the robot detects physical changes in 3D and shares that information with a human in real-time through augmented reality, who is then able to evaluate the information received and decide follow-on action.
Human facial expressions could be one of the keys in building trust between Soldiers and autonomous agents. In typical studies, researchers define groups of people, and either specify and examine variables within those groups or apply variables as measures that tend to represent entire population samples by simple group tendencies. In these traditional constructs, researchers treat sample variability within the experimenter-specified groups as noise. The Army researchers proposed a new approach, using flexible mixture modeling, which inferred and sorted individuals into four sub-groups by observed differences in traits (e.g., age, personality and maladaptive coping strategies associated with uncertainty) and states (e.g., initial trust, stress and workload perceptions).
Artificial Intelligence, or AI-enabled systems are a part of everyday life -- people use AI software to figure out the best way to navigate to new places, ask virtual agents in phones to answer questions, as well as have robots patrol the supermarket to ensure shelves stay stocked. The military is no exception -- it expects robots to do dull, dirty dangerous jobs. AI support tools help solve complicated problems.
At the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, researchers developed Transparent Multi-Modal Crew Interface Designs, which are part of the laboratory’s Human-Autonomy Teaming Essential Research Program. The project involves the development of technologies that support Soldiers’ ability to team with AI-enabled robotic tanks.
Dialogue is one of the most basic ways humans use language, and is a desirable capability for autonomous systems. Army researchers developed a novel dialogue capability to transform Soldier-robot interaction and perform joint tasks at operational speeds. The fluid communication achieved by dialogue will reduce training overhead in controlling autonomous systems and improve Soldier-agent teaming.
Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, in collaboration with the University of Southern California’s Institute for Creative Technologies, developed the Joint Understanding and Dialogue Interface, or JUDI, capability, which enables bi-directional conversational interactions between Soldiers and autonomous systems.
The U.S. Army Combat Capabilities Development Command’s Army Research Laboratory designated several research programs as essential for future Soldier capabilities. Of these major flagship programs, the Human Autonomy Teaming, or HAT, Essential Research Program focuses on the relationship between humans and AI and how to establish human-robot teams that can survive and function in complex environments.
According to the researchers, AI and machine learning technologies enable the processing of large volumes of data very rapidly, which allows warfighters to make decisions faster and with greater confidence. This increase in processing capability will also accelerate the pace of actions on the battlefield and result in the emergence of even more complex situations to consider.
As the U.S. Army revamps its small arms training and raises rifle qualification standards, researchers are studying alternative training and tools to help improve Soldier performance.
he U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, Army Medical Department Field Element, located at Fort Sam Houston, Texas,
The human brain’s activity, in its complexity, cannot be summarized with any single method -- researchers must take into account the chemical, electrical and historical aspects of brain activity -- from neural communication to learning and development. Army scientists from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory along with academics at the Salk Institute for Biological Studies in La Jolla, California, are researching new methods to characterize brain activity to predict future behavior or cognitive state, borrowing methods from clinical settings. While studying the possibility of a new method of brain dynamics, they said they have been successful in predicting clinical seizures using recording electrodes placed directly on brain tissue.