AgiBot has introduced GE-Act 2.0, a native world-action model that has been pretrained from random initialization using embodied data. This new model has significantly scaled from 300 to 30,000 hours of training, enabling it to perform zero-shot skills, including towel folding, across two different robot embodiments.
The release of GE-Act 2.0 is significant as it demonstrates AgiBot's commitment to advancing robotic capabilities through extensive data scaling. By increasing the training hours, the model can now execute complex tasks without prior specific training, showcasing the potential for greater versatility in robotic applications.
Looking ahead, it will be important to monitor how GE-Act 2.0 performs in real-world scenarios and whether it can be adapted for additional tasks beyond towel folding. No further timeline was disclosed at the time of publication.
Editor's Note
The introduction of GE-Act 2.0 by AgiBot highlights a pivotal moment in the robotics sector, emphasizing the importance of data scaling in enhancing robotic functionalities. As companies increasingly seek to integrate advanced AI models into their operations, the ability to perform zero-shot tasks will be a key competitive advantage in various applications.
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