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Alibaba Debuts Qwen-Robot Series with Three Embodied Large Models to Solve Heterogeneous Robot Adaptation
On June 16, Alibaba unveiled the Qwen-Robot series of embodied intelligence models, comprising three core components: the VLA manipulation model Qwen-RobotManip, the VLN navigation model Qwen-RobotNav, and the world model Qwen-RobotWorld. This strategic release reflects a deeper commitment from major tech players to embodied intelligence foundation models, enabling seamless coordination across robot control, navigation, and physical reasoning.
To overcome the industry pain point where traditional VLA models struggle with hardware or scenario changes, Qwen-RobotManip introduces an 80-dimensional unified action representation that defines a universal "body language" for various hardware types, allowing automatic adaptation across devices with just a few feedback steps. The VLN model Qwen-RobotNav, designed for navigation and task execution, builds on Qwen-VL and, for the first time, consolidates five task families—including language-guided navigation, target search, and autonomous driving—into a single framework, removing the need for model switching in complex tasks.
As the cognitive core, Qwen-RobotWorld grants the system the ability to reason about the physical world, predicting and simulating subsequent actions and states. Embodied intelligence is currently transitioning from single-scenario to generalized applications. With all three models released together, Alibaba is poised to accelerate the practical deployment of heterogeneous robots by decoupling the technical architecture and integrating multimodal capabilities.
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On June 16, Alibaba unveiled the Qwen-Robot series of embodied intelligence models, comprising three core components: the VLA manipulation model Qwen-RobotManip, the VLN navigation model Qwen-RobotNav, and the world model Qwen-RobotWorld. This strategic release reflects a deeper commitment from major tech players to embodied intelligence foundation models, enabling seamless coordination across robot control, navigation, and physical reasoning.
To overcome the industry pain point where traditional VLA models struggle with hardware or scenario changes, Qwen-RobotManip introduces an 80-dimensional unified action representation that defines a universal "body language" for various hardware types, allowing automatic adaptation across devices with just a few feedback steps. The VLN model Qwen-RobotNav, designed for navigation and task execution, builds on Qwen-VL and, for the first time, consolidates five task families—including language-guided navigation, target search, and autonomous driving—into a single framework, removing the need for model switching in complex tasks.
As the cognitive core, Qwen-RobotWorld grants the system the ability to reason about the physical world, predicting and simulating subsequent actions and states. Embodied intelligence is currently transitioning from single-scenario to generalized applications. With all three models released together, Alibaba is poised to accelerate the practical deployment of heterogeneous robots by decoupling the technical architecture and integrating multimodal capabilities.
Swedish AI Startup Lovable Eyes $13.2 Billion Valuation After Major Funding Round
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