Opentrons, NVIDIA Partner to Advance Physical AI for Laboratory Robotics

The collaboration focuses on integrating AI-driven hypothesis generation with automated wet-lab validation, a step widely viewed as a bottleneck in modern biotech research.

Opentrons Labworks has entered into a partnership with NVIDIA to accelerate the development of Physical AI for laboratory robotics, aiming to bridge the gap between computational biology and real-world experimental execution.

The collaboration focuses on integrating AI-driven hypothesis generation with automated wet-lab validation, a step widely viewed as a bottleneck in modern biotech research.

Under the partnership, Opentrons will combine its global fleet of more than 10,000 laboratory robots with NVIDIA’s Isaac and Cosmos platforms. The integration is designed to enable what the companies describe as a “closed loop” system, where artificial intelligence models not only generate predictions but also validate them through physical experiments carried out by robotic systems in the lab.

The collaboration comes at a time when advances in computational biology have significantly outpaced experimental capacity. AI models, including those built on NVIDIA BioNeMo, are now capable of generating molecular hypotheses faster than they can be tested by human scientists. As a result, experimental execution has emerged as a rate-limiting step in drug discovery and biological research.

Opentrons’ platform is positioned to address this challenge through standardized, API-driven lab automation. Unlike traditional laboratory automation systems that are often custom-built and costly, Opentrons’ robots are designed to be affordable and scalable. The company reports that its systems are deployed across every top-20 US research university and 14 of the top 15 global biopharma companies, forming what it describes as the world’s largest standardized network of lab automation.

The technical framework of the partnership centers on a continuous feedback loop. In this model, an AI agent proposes a molecular structure and experimental plan, robotic systems execute the experiment, and the resulting data is fed back into the AI model to refine subsequent hypotheses. The approach is intended to allow AI systems to learn directly from real-world experimental outcomes rather than relying solely on simulated or historical data.

According to NVIDIA, standardization is a key component of the initiative. “Connecting computational models with experimental validation is essential,” said Stacie Calad-Thomson, NVIDIA’s healthcare lead. She noted that consistent and reproducible data remains a challenge in manual lab workflows, limiting the effectiveness of AI-driven discovery.

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