"If a robot acts as a teammate, tasks can be accomplished faster and more situational awareness can be obtained," said Maggie Wigness, from ARL.
"Further, robot teammates can be used as an initial investigator for potentially dangerous scenarios, thereby keeping Soldiers further from harm," said Wigness.
Researchers focused their initial investigation on learning robot traversal behaviours with respect to the robot's visual perception of terrain and objects in the environment.
More specifically, the robot was taught how to navigate from various points in the environment while staying near the edge of a road, and also how to traverse covertly using buildings as cover.
This is done by leveraging inverse optimal control, also commonly referred to as inverse reinforcement learning, which is a class of machine learning that seeks to recover a reward function given a known optimal policy.
These trajectory exemplars are then related to the visual terrain/object features, such as grass, roads and buildings, to learn a reward function with respect to these environment features.
While similar research exists in the field of robotics, what ARL is doing is especially unique.
"The challenges and operating scenarios that we focus on here at ARL are extremely unique compared to other research being performed," Wigness said.
"We seek 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," she said.
The research is crucial for the future battlefield, where soldiers will be able to rely on robots with more confidence to assist them in executing missions.
(This story has not been edited by Devdiscourse staff and is auto-generated from a syndicated feed.)