There’s a biological component that allows humans and other mammals to navigate complex environments: Our human "central pattern generators" (CPG) are neural networks that produce rhythmic patterns of control signals for limbs, using simple environmental cues.
University of Pittsburg engineers have received a $1,606,454 award from the National Science Foundation to lead a two-year project to engineer these patterns and signals in neural networks in robots.
Fully functional robots with biomimetic sensorimotor control
Neuromorphic engineering—computing inspired by the human brain—will be key to achieving efficient, adaptive sensorimotor control in these robots, says Rajkumar Kubendran, principal investigator and assistant professor of electrical and computer engineering at Pitt.
“We aim to demonstrate a fully functional quadropod or hexapod robot that can learn to move, using principles informed by neuroscience, leading to biomimetic sensorimotor control for energy-efficient locomotion, and using learning algorithms running on bio-realistic neural networks,” Kubendran said.
Critical uses include disaster response
"Agile robots that can explore unknown and treacherous terrains have the potential to enable autonomous navigation for commercial transport, enhance disaster response during floods and earthquakes or to remote and unsafe areas like malfunctioning nuclear plants or space exploration,” he said.
The project, set to begin in 2024, is part of a larger $45 million initiative by the NSF to invest in the future of semiconductors.