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

TL;DR

MolmoSpaces is an extensive open ecosystem with over 230,000 diverse indoor environments and rich annotations, designed to facilitate large-scale benchmarking and development of robust robot navigation and manipulation policies.

Contribution

It introduces MolmoSpaces, a large-scale, simulator-agnostic ecosystem with diverse environments and a benchmark suite supporting various embodied tasks for robot learning research.

Findings

Strong sim-to-real correlation (R=0.96) and high reproducibility (ρ=0.98).

Newer policies outperform earlier versions in benchmarks.

Identifies sensitivities to prompt phrasing, initial joint positions, and occlusion.

Abstract

Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that characterize real environments are vast and underrepresented in existing robot benchmarks. Measuring this level of generalization requires infrastructure at a scale and diversity that physical evaluation alone cannot provide. We introduce MolmoSpaces, a fully open ecosystem to support large-scale benchmarking of robot policies. MolmoSpaces consists of over 230k diverse indoor environments, ranging from handcrafted household scenes to procedurally generated multiroom houses, populated with 130k richly annotated object assets, including 48k manipulable objects with 42M stable grasps. Crucially, these environments are simulator-agnostic, supporting popular options such as MuJoCo, Isaac, and ManiSkill. The ecosystem supports the full spectrum of embodied tasks: static and mobile manipulation, navigation, and multiroom long-horizon tasks requiring coordinated perception, planning, and interaction across entire indoor environments. We also design MolmoSpaces-Bench, a benchmark suite of 8 tasks in which robots interact with our diverse scenes and richly annotated objects. Our experiments show MolmoSpaces-Bench exhibits strong sim-to-real correlation (R = 0.96, \r{ho} = 0.98), confirm newer and stronger zero-shot policies outperform earlier versions in our benchmarks, and identify key sensitivities to prompt phrasing, initial joint positions, and camera occlusion. Through MolmoSpaces and its open-source assets and tooling, we provide a foundation for scalable data generation, policy training, and benchmark creation for robot learning research.