"Deep learning allows machines to take vast amounts of data and distil useful rules and lessons all by themselves," Preferred Networks chief executive officer Toru Nishikawa said.
"For a robot, that means understanding not only why one movement was successful and another one not, but also how to improve its performance."
Fanuc plans to make the self-learning functionality commercially available next year, Nishikawa said. The two companies are also working on applications that predict machine failures to prevent costly stoppages.
The deep learning algorithms, inspired by the way living things process information, already help minimise human involvement when Facebook tags user photos or Google serves up ads. Now hardware companies from Fanuc to Toyota and Samsung are stepping up investments to add problem-solving capabilities to their products.
Fanuc this year paid 900 million ($11 million) for a 6 per cent stake in Preferred Network, after rival ABB invested several million dollars into AI startup Vicarious. Facebook's Mark Zuckerberg, Amazon.com's Jeff Bezos, actor Ashton Kutcher and Samsung are among shareholders.
Nishikawa is a world finalist of the prestigious ACM Programming Contest, whose winners include a former chief technology officer of Facebook and the first employee at Google. He founded Preferred Networks with his University of Tokyo classmate Daisuke Okanahara in March 2014.
Little more than a year later, the company counted Fanuc, Toyota and Panasonic among its partners.
"This technology doesn't have to be limited to Fanuc robots," Nishikawa said. "There are all kinds of machines that can benefit from it."