Soldiers could teach future robots how to outperform humans | Robotics - Science Daily

Summary:
Researchers have designed an algorithm that allows an autonomous ground vehicle to improve its existing navigation systems by watching a human drive. 

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In the future, a Soldier and a game controller may be all that's needed to teach robots how to outdrive humans, says Science Daily.

Army researchers use human teachers to improve navigation in autonomous systems.
Photo:  U.S. Army Research Laboratory

At the U.S. Army Combat Capabilities Development Command's Army Research Laboratory and the University of Texas at Austin, researchers designed an algorithm that allows an autonomous ground vehicle to improve its existing navigation systems by watching a human drive. The team tested its approach -- called adaptive planner parameter learning from demonstration, or APPLD -- on one of the Army's experimental autonomous ground vehicles.

"Using approaches like APPLD, current Soldiers in existing training facilities will be able to contribute to improvements in autonomous systems simply by operating their vehicles as normal," said Army researcher Dr. Garrett Warnell. "Techniques like these will be an important contribution to the Army's plans to design and field next-generation combat vehicles that are equipped to navigate autonomously in off-road deployment environments."

The researchers fused machine learning from demonstration algorithms and more classical autonomous navigation systems. Rather than replacing a classical system altogether, APPLD learns how to tune the existing system to behave more like the human demonstration...

"From a machine learning perspective, APPLD contrasts with so called end-to-end learning systems that attempt to learn the entire navigation system from scratch," Stone said. "These approaches tend to require a lot of data and may lead to behaviors that are neither safe nor robust. APPLD leverages the parts of the control system that have been carefully engineered, while focusing its machine learning effort on the parameter tuning process, which is often done based on a single person's intuition."
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Additional resources  
Journal Reference:  

1. Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, Peter Stone. APPLD: Adaptive Planner Parameter Learning From Demonstration. IEEE Robotics and Automation Letters, 2020; 5 (3): 4541 DOI: 10.1109/LRA.2020.3002217

Source: Science Daily