训练数据短缺制约人形机器人发展

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Robotics; Generative AI; News

AI models train on information available on the internet. Robots can’t do that.

September 23, 2026

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.”

Related:China leads dexterous hand supply for humanoids

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.

“Bad data definitely can cause a lot more issues in the real world than with a chatbot where it just gives you the wrong information, which could be very problematic as well.,” she said. “But in the physical world … A huge issue is if that robot falls and is flailing around and it's in a crowded mall environment.”

About the Authors

Shaun Sutner

Shaun Sutner, a journalist with more than 25 years of daily newspaper experience and 12 years at Informa TechTarget as an editor and writer, directs news coverage for AI Business. He was previously a senior news and features writer covering health IT and HR software at TechTarget and a senior news director overseeing coverage of AI, business analytics, data management and government tech regulation.

Sutner's newspaper career included investigative reporting and covering the Massachusetts State House and politics for the Worcester Telegram & Gazette. He has written about snow sports as a T&G columnist and correspondent for 21 years. Sutner's interests also include tennis, standup paddleboarding, cooking and popular music.

Esther Shittu

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