Reported by TechCrunch AI
Alex de Vigan’s last business got swamped by the rise of generative AI, but now he’s betting the tech that team had built, a platform for creating tempting product renderings for e-commerce, can be re-purposed to make robots more useful.
His new venture, Physicl , has raised $10 million in seed funding from Inovia Capital to help world model designers and robot builders solve thorny training problems with more realistic simulations.
Founder and CEO de Vigan spent the last decade building nfinite, which made it easy for brands and retailers to create 3D models of their goods and display them online. The arrival of transformer-based AI models led that company to focus more on generative imagery, but de Vigan saw a new purpose for his team and the tech stack they’d built to faithfully render 3D environments.
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“What we were super unique about doing in 3D, AI was going to replace, but something was missing in AI: its ability to understand, interact, and manipulate space and objects,” de Cigan says.
Researchers developing physical AI models to make robots more capable of tasks like handling diverse, small objects have been frustrated by the returns of training them on video, and the expense of gathering data through remote operations.
Simulations in digital worlds are seen as a promising solution, but ensuring that these simulated worlds are faithful to real-world physics has proven difficult. de Vigan believes that the pipeline his previous company built to “transform unstructured, incomplete, heterogeneous real-world information into a high-fidelity physical representation… with correct geometry, materials, and physics” can now be used to make physical AI models more effective.
Physcl is entering a crowded category of startups vying to provide data and tooling to foundation model builders and robot operators. “The space is very young and and and very blurry sometimes,” de Vigan admits. There are a variety of companies collecting, labeling, or generating data in different modalities as their customers try to figure out the secret sauce that will make robots live up to their promise.
In particular, Phyiscl wants to focus on post-training models on difficult edge…