Soren

Training robots for high-stakes tasks, such as surgery, is bottlenecked by data. Real surgical data is scarce, hard to label, and too risky to generate through trial and error. This gap is even worse for rare complications and edge cases, which are exactly the scenarios where robotic precision matters most. This slows the development of safe autonomous systems and limits their potential to improve precision and expand access to high-quality surgical care. Our project addresses this gap by creating a scalable source of realistic surgical training data. We developed a reinforcement learning environment-generation harness that leverages Astra’s spatial intelligence to construct high-fidelity surgical scenes with variable anatomy, deformable tissue behavior, procedural conditions, and failure cases. Previously, environments of this quality were difficult to generate at scale, particularly with reliable, task-specific reward signals. Astra’s strong spatial reasoning makes both possible, enabling robotic systems to train and validate on complex, high-risk scenarios safely in simulation. By bringing the capabilities of frontier AI models into surgical robotics, we hope to accelerate the path toward safer autonomous systems and, ultimately, make high-quality surgical care more precise and accessible to more people.