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An autonomous systems research will provide reliable robot teammates to soldiers. The מew development could be crucial for the future battlefield, where soldiers will be able to rely on robots with more confidence to assist them in executing missions. The U.S. Army Research Laboratory (ARL) and the Robotics Institute at Carnegie Mellon University developed a new technique to quickly teach robots novel traversal behaviors with minimal human oversight: a prototype of robots that can navigate autonomously in environments while carrying out actions a human would expect of the robot in a given situation.
ARL researcher Dr. Maggie Wigness said: “Robot teammates can be used as an initial investigator for potentially dangerous scenarios, thereby keeping Soldiers further from harm.” To achieve this, the robot must be able to use its learned intelligence to perceive, reason and make decisions, reports defence-blog.com.
“This research focuses on how robot intelligence can be learned from a few human example demonstrations,” Wigness said. “The learning process is fast and requires minimal human demonstration, making it an ideal learning technique for on-the-fly learning in the field when mission requirements change.”
While similar research exists in the field of robotics, what ARL is doing seeks to create intelligent robotic systems that reliably operate in warfighter environments, “meaning the scene is highly unstructured, possibly noisy, and we need to do this given relatively little a priori knowledge of the current state of the environment.” “The fact that our problem statement is so different than so many other researchers allows ARL to make a huge impact in autonomous systems research. Our techniques, by the very definition of the problem, must be robust to noise and have the ability to learn with relatively small amounts of data.”
According to Wigness, this preliminary research has helped the researchers demonstrate the feasibility of quickly learning an encoding of traversal behaviors. In the future, the researchers will try to use a priori intelligence that may be available about an environment.