Generalist Releases GEN-1 Model to Enable Robots in Laundry Folding and Kitting Tasks
Silicon Valley startup Generalist has unveiled its GEN-1 model, designed to enhance off-the-shelf robots for tasks like folding laundry and packing items. The company, backed by investors including Nvidia's NVentures, raised $140 million in 2025. Founders draw parallels to AI advancements in language models.
National Center for Advancing Translational Sciences from Bethesda, MD / Wikimedia (Public domain)Generalist, a Silicon Valley startup developing robot brains, released a new model called GEN-1. The model enables off-the-shelf robots to handle tasks including folding laundry and kitting, according to Pete Florence, the company's CEO. Forbes reported that GEN-1 supports improvisation in unpredictable scenarios, such as adjusting a snagged item during packing.
In a demonstration, a robot equipped with a pair of disembodied arms and crablike pincers picked up a bag, shook it to adjust a snagged toy potted plant plushie, and placed it back on a conveyor belt. The robot had been instructed to open plastic bags on the conveyor belt and stuff toy potted plant plushies inside them. This action showed the robot improvising beyond its programmed task.
Pete Florence, who serves as CEO of Generalist, posed for a photo in the company's office with co-founder Andy Zeng. Florence founded Generalist with Google co-worker Andy Zeng and Boston Dynamics roboticist Andy Barry. Florence previously worked as a lead on PaLM-E, one of Google’s foundational robotics papers.
Generalist raised $140 million at a $440 million valuation in 2025. The startup's backers include Spark Capital, Nvidia’s NVentures, Bezos Expeditions, and Boldstart Ventures. The company's offices are located in San Mateo.
” He added, “The limerick didn’t exist before. To achieve that, you need an improvisational level of intelligence. ” Nvidia CEO Jensen Huang declared robots were entering the ChatGPT era last year. ” These data hands turn a person’s hands into pincer-like robot hands and collect visual and sensory data.
Data hands can be used in homes, warehouses, and workplaces to perform everyday tasks, according to Generalist. At Generalist’s offices in San Mateo, data hands operators work side-by-side with researchers practicing tasks like bundling a bouquet of flowers or futzing around with electronics. Generalist’s dataset from data hands exceeds half a million hours, Pete Florence said.
Robots using Generalist’s models can fold boxes nearly as fast as humans. These robots fold boxes roughly three times faster than competing systems, according to Florence. Years before Generalist existed, co-founder Andy Zeng noticed someone picking up trash with a simple grabber tool while walking in Newport Beach.
This observation inspired the development of data hands. Physical Intelligence, a competitor, pairs off-the-shelf robotics hardware with transformer-based AI models. Physical Intelligence is reportedly raising $1 billion at an $11 billion valuation.
Fraser Kelton, an investor in Generalist at Spark Capital, previously led product at OpenAI during the commercialization of GPT-3 and ChatGPT. ” He continued, “But since then, every time they've scaled up these models, the returns on generalization have been profound…And all of a sudden the language model companies that were building vertical or domain-specific models have been eclipsed.
” GPT-2 was released in 2019, according to Kelton.
Key Facts
Story Timeline
6 events- 2026-04-02
Generalist releases GEN-1 model for robot tasks
1 source@Forbes - 2025
Generalist raises $140 million at $440 million valuation
1 source@Forbes - 2025
Nvidia CEO Jensen Huang declares robots entering ChatGPT era
1 source@Forbes - 2019
GPT-2 released
1 source@Forbes - Years before Generalist existed
Andy Zeng notices trash pickup with grabber tool in Newport Beach
1 source@Forbes - Undated recent
Physical Intelligence reportedly raising $1 billion at $11 billion valuation
1 source@Forbes
Potential Impact
- 01
Competition intensification between Generalist and firms like Physical Intelligence in AI robotics market.
- 02
Increased adoption of AI-driven robots in warehouses and homes due to improved task generalization.
- 03
Broader application of transformer-based models beyond text to physical tasks.
- 04
Potential shift in robotics investment toward data scaling over hardware innovation.
- 05
Efficiency gains in tasks like box folding, potentially reducing human labor needs in specific sectors.
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