Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation. Every demonstration requires specialized equipment, clinical expertise, and access to patients or laboratory environments. This creates three fundamental challenges for developers.
First is the data gap. Training modern robotic policies requires demonstrations across diverse anatomies and procedures. Most teams have only hundreds of demonstrations—not the tens of thousands needed to build robust systems. But even if collecting millions of demonstrations were practical, the most important cases would still be missing. This is because healthcare is dominated by the long tail. Rare anatomies, challenging patient physiology, complications, and failure modes occur too infrequently to be adequately represented in real-world datasets. Yet, these are the cases that matter most for clinical safety.
This data gap leads directly to the second challenge: generalization. Imitation learning inevitably plateaus at the edges of the data distribution. Reinforcement learning (RL) offers a path beyond that plateau. RL can explore millions of interactions, stress-test policies, and learn from failures. To do so effectively, it needs simulation that is realistic enough to produce meaningful policies—and fast and scalable enough to train at scale.
