Alibaba Debuts Suite of AI Models for Robots

Chinese tech giant Alibaba unveiled its first suite of artificial intelligence models for robots, according to a Monday (June 15) company blog post.

The Qwen Robot Suite comes as AI companies shift away from chatbots and into physical AI.

“The Qwen family of foundation models already gives strong perception and reasoning about the physical world,” the post said. “But seeing is not acting. The gap between vision and language understanding and physical control remains the central bottleneck for embodied intelligence.”

The Qwen Robot Suite’s three models close this gap, per the post. Qwen-RobotNav helps robots understand how to navigate physical spaces, while Qwen-RobotWorld is a video “world model” that allows a robot to predict how a physical scenario will unfold.

Lastly, Qwen-RobotManip “turns heterogeneous robot data into a coherent canonical space, enabling cross-embodiment training at scale,” the post said.

“Together, they enable an agentic system where general intelligence translates directly into physical action,” the post said.

The launch comes one week after a report that Alibaba Group formed a new business unit known as Token Foundry as it reorganizes to strengthen its AI efforts.

Led by Alibaba CEO Eddie Wu, Token Foundry will combine Alibaba’s Tongyi Lab and Future Life business units, and operate under the company’s recently created Alibaba Token Hub.

In other physical AI news, last week saw the launch of Nvidia’s Cosmos 3 foundational model for physical AI. Nvidia Founder and CEO Jensen Huang said at the launch that “the big bang of physical AI is just around the corner thanks to breakthroughs in multimodal reasoning language, vision and world models.”

The distinction is important for anyone building or deploying physical AI. While a large language model learns from text, a world foundation model learns from physical environments.

For robots, that means learning to handle objects with the help of millions of interaction examples, while an autonomous vehicle needs exposure to rare and dangerous scenarios that can’t be safely or cheaply collected at scale on public streets.

“World foundation models solve this by generating synthetic training data that reflects real physics,” PYMNTS reported at the time. “Instead of driving a test fleet for years, an autonomous vehicle developer can run millions of simulated scenarios in days.”

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