NVIDIA shares how to evaluate general-purpose robot policies for real-world deployment

Robotics foundation models have become capable enough to follow natural language instructions for manipulation tasks, but evaluating whether these models will work in real-world deployment remains extremely difficult. Current simulation-based benchmarks have critical flaws: they often train and test models on the same visual environments, leading models to memorize rather than generalize; they use fixed task sets that quickly become saturated as models improve, making it hard to distinguish which systems are actually more capable; and they provide only binary success/failure scores without explaining why failures occur. A simulation benchmarking platform called RoboLab addresses these issues by enabling rapid generation of diverse new tasks to prevent saturation, providing detailed diagnostic tools to understand failure modes, and using statistical methods to ensure results are trustworthy rather than based on small sample sizes.