HomeGenesis AI Master Robot Tasks With Single Model, Marking a ChatGPT-Like Moment
As global interest in robotics accelerates, Genesis AI has introduced GENE-26.5, its inaugural robot foundation model. This launch represents a major leap forward in general-purpose robotics, particularly in managing intricate and unstructured tasks.
Recent public demonstrations highlight the robot’s impressive agility and independence. From delicate operations like beating eggs with one hand to logic-driven challenges such as solving a Rubik’s cube, rhythmic precision tasks like playing the piano, and routine kitchen duties like slicing tomatoes, the robot executes each with accuracy. Crucially, these diverse capabilities are powered by a single base model rather than a collection of specialized ones, enabling fully autonomous, end-to-end operation without human oversight.
This technological edge is rooted in the team’s extensive R&D background. Having previously developed the widely recognized Genesis physics engine, Genesis AI has achieved full-stack in-house development across hardware design, control systems, data collection, model training, and simulation. This closed-loop approach drastically boosts training efficiency: the team reports that most complex motor skills can be learned using less than an hour of task-specific robot data.
The market has responded with strong confidence. In mid-2025, the company secured a $105 million seed round, driven by its innovative technical architecture and setting a new industry record for the largest seed funding in robotics.
According to the Genesis AI team, this milestone is merely the start. As the base model continues to advance, they plan to unveil their first full-body general-purpose robot soon. This progression hints at a future where robots transition from labs to real-world environments, performing tasks with human-like dexterity and adaptability.
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As global interest in robotics accelerates, Genesis AI has introduced GENE-26.5, its inaugural robot foundation model. This launch represents a major leap forward in general-purpose robotics, particularly in managing intricate and unstructured tasks.
Recent public demonstrations highlight the robot’s impressive agility and independence. From delicate operations like beating eggs with one hand to logic-driven challenges such as solving a Rubik’s cube, rhythmic precision tasks like playing the piano, and routine kitchen duties like slicing tomatoes, the robot executes each with accuracy. Crucially, these diverse capabilities are powered by a single base model rather than a collection of specialized ones, enabling fully autonomous, end-to-end operation without human oversight.
This technological edge is rooted in the team’s extensive R&D background. Having previously developed the widely recognized Genesis physics engine, Genesis AI has achieved full-stack in-house development across hardware design, control systems, data collection, model training, and simulation. This closed-loop approach drastically boosts training efficiency: the team reports that most complex motor skills can be learned using less than an hour of task-specific robot data.
The market has responded with strong confidence. In mid-2025, the company secured a $105 million seed round, driven by its innovative technical architecture and setting a new industry record for the largest seed funding in robotics.
According to the Genesis AI team, this milestone is merely the start. As the base model continues to advance, they plan to unveil their first full-body general-purpose robot soon. This progression hints at a future where robots transition from labs to real-world environments, performing tasks with human-like dexterity and adaptability.
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Apple unveils iOS 27 with local AI and Google partnership boosting Siri
The Information recently highlighted Apple’s strategic approach to integrating artificial intelligence into the upcoming OS 27. By leveraging Google’s Gemini model to train a more efficient, lightweight AI, Apple aims to deliver robust local edge AI
Perplexity Computer Unveils Hybrid Inference to Balance Privacy and Efficiency
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