Generalist's GEN-0, GEN-1 and GEN-1.5 robot models: claims and access
Overview
Generalist says its GEN-0, GEN-1 and GEN-1.5 robot foundation models can learn a new physical task from a short demonstration, without a dedicated dataset.
The company reports roughly 99% success on selected tasks across these models and is moving toward in-context physical prompting, in which a robot is shown an example and then does the work. The Sequence says it remains an open question whether those company-reported gains hold up across complete, long-horizon jobs in real deployments.
On October 29, 2026, Generalist announced it is expanding access to GEN-1.5 and taking sign-ups through a waitlist. Its post on X links to a playlist of other tasks the models can perform and a blog post about GEN-1.5.
Written by AI from the articles below · updated Oct 29, 10:08 AM ET
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Developments
2 developments
- Oct 29, 10:03 AM ET · 1 articleGeneralist expands access to its GEN-1.5 physical-world foundation model via waitlistGeneralist: Generalist expands access to its GEN-1.5 physical-world foundation model
- Oct 23, 7:03 AM ET · 1 articleGeneralist builds robot foundation models GEN-0, GEN-1, and GEN-1.5 for promptable physical tasksThe Sequence: Generalist's GEN-1.5 pushes robots toward learning tasks from a short demonstration
Article timeline
The articles in this story. Times are ET.
Generalist@GeneralistAIOfficialGeneralist expands access to its GEN-1.5 physical-world foundation modelAIGeneralist is expanding access to GEN-1.5, its latest foundation model for the physical world, and is taking sign-ups through a waitlist. The post links to a playlist of other tasks its models can perform and a blog post about GEN-1.5.
- The SequenceBlogGeneralist's GEN-1.5 pushes robots toward learning tasks from a short demonstration
AIGeneralist is building robot foundation models trained on large-scale physical data, so new tasks may need only a short demonstration rather than a dedicated dataset. Across GEN-0, GEN-1, and GEN-1.5, the company reports roughly 99% success on selected tasks and moves toward in-context physical prompting. The source notes that whether these company-reported gains hold up across complete, long-horizon jobs in real deployments remains an open question.
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