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Fable 5.2 "Crash" Ant Lingbo Tactile Model, CEO Zhu Xing: The collaboration of the two types of models will accelerate the maturity of the embodied industry

On November 10, the same day Claude Fable 5.2 was released, Ant Lingbo Technology open sourced the basic tactile model LingBot-Touch. A general large model for the digital world and an embodied basic model for closed-loop physical operations came into public view on the same day. The relationship between these two technical routes is the focus of current discussions in the embodied intelligence industry. In Ant Lingbo's view, the general large model and the embodied basic model are closer to a 1+1>2 synergistic relationship: the former enhances the understanding and reasoning of tasks and environments, and the latter complements physical perception and closed-loop operation capabilities. Only when the two go hand in hand can they jointly accelerate the maturity of the embodied industry. The open source LingBot-Touch is based on nearly 5,000 hours of real tactile data pre-training and covers three mainstream sensing principles: magnetic tactile, visual tactile and piezoresistive. It can convert different signals into the same set of tactile representations for use by different dexterous hands and action models, allowing the robot to "see clearly and think clearly" while further complementing the tactile perception required for fine operations. Previously, Ant Lingbo's universal brain has covered high-precision spatial perception, highly dynamic environment interaction and generalizable dexterous operations. This time, LingBot-Touch has been connected to LingBot-VLA, extending the sensing capabilities to the "fingertips", making the robot "touch more sensitive", and the hand-eye coordination ability of the native universal brain has been further improved. 26 real-machine tests, the task success rate increased by 14 to 26 percentage points. As general-purpose large models continue to advance, the robot's ability to understand complex tasks and reason about operation steps is increasing. But in the real physical world, every time a robot performs an action, it must sense changes in objects and the environment, and then correct the next action. In many delicate operations, the robot can clearly see where the object is.

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