MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation

Deploying robots at scale demands robustness to the long tail of everyday situations. We introduce MolmoSpaces, a fully open ecosystem to support large-scale benchmarking of robot policies. MolmoSpaces consists of over 230,000 diverse indoor environments populated with 130,000 richly annotated object assets, including 48,000 manipulable objects with 42 million stable grasps. The ecosystem supports the full spectrum of embodied tasks: static and mobile manipulation, navigation, and multiroom long-horizon tasks. Our experiments show MolmoSpaces-Bench exhibits strong sim-to-real correlation (R = 0.96, rho = 0.98), providing a foundation for scalable data generation, policy training, and benchmark creation for robot learning research.