Lack of training data stifling humanoid bot development

Robotics has a big problem.

While the rapid development of generative AI technology over the last four years has supercharged the global robotics industry and accelerated testing and deployment of humanoid and other commercial bots, robot developers are at somewhat of an impasse because of the shortage of training data.

“Large language models … basically, they ingested all of the internet,” said Sce Pike, vice president of AI growth and solutions at Canada-based technology company Telus Digital, on the Targeting AI podcast from AI Business.

Pike, who is leading Telus Digital’s robotics and world model projects, referred to former Meta AI chief scientist Yann LeCun’s well-known analogy comparing the actual intelligence of an AI model to a preschooler’s.

LeCun “looks at the physical AI world as something that is almost beyond reach of just internet-based data,” Pike continued. “He compares it to a 4-year-old, who actually has about five times more comprehension than what an LLM has. The way that a 4-year-old learns is through visual input. A 4-year-old is using their visual ocular nerves and sensory input to understand how physics works, how gravity works, how cause and effect works. You just don't get that by just reading text.”

One of the biggest barriers to training today’s advanced robots and the world models that guide them in the real world is the sheer quantity and exorbitant compute and data storage cost of video footage robot developers require, Pike said. Another impediment is the clashing array of different cameras, lidar devices and infrared sensors used to collect physical data to train AI models for robots.

“It is causing so much noise,” Pike said.

Pike is working on one of the biggest problems in humanoid robotics -- physical safety.

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Senior News Director, AI Business

News Writer, AI Business