New algorithm encourages robots to move more randomly to collect more diverse data for learning. In tests, robots started with no knowledge and then learned and correctly performed tasks within a single attempt. New model could improve safety and practicality of self-driving cars, delivery drones and more.
Northwestern University engineers have developed a new artificial intelligence algorithm designed specifically for smart robotics. By helping robots rapidly and reliably learn complex skills, the new method could significantly improve the practicality -- and safety -- of robots for a range of applications, including self-driving cars, delivery drones, household assistants and automation.
Berrueta is a Presidential Fellow at Northwestern and a Ph.D. candidate in mechanical engineering at the McCormick School of Engineering. Robotics expert Todd Murphey, a professor of mechanical engineering at McCormick and Berrueta's adviser, is the paper's senior author. Berrueta and Murphey co-authored the paper with Allison Pinosky, also a Ph.D. candidate in Murphey's lab.
Perhaps even more impressive: Robots using the MaxDiff RL method often succeeded at correctly performing a task in a single attempt. And that's even when they started with no knowledge. "This doesn't have to be used only for robotic vehicles that move around," Pinosky said."It also could be used for stationary robots -- such as a robotic arm in a kitchen that learns how to load the dishwasher. As tasks and physical environments become more complicated, the role of embodiment becomes even more crucial to consider during the learning process. This is an important step toward real systems that do more complicated, more interesting tasks.
Robotics Research Engineering And Construction Physics Robotics Artificial Intelligence Computer Modeling Computers And Internet
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