Robotics: Science and Systems XXII

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

Yejin Kim, Wilbert Pumacay, Omar Rayyan, Max Argus, Winson Han, Eli Vanderbilt, Jordi Salvador, Abhay Deshpande, Rose Hendrix, Snehal Jauhri, Shuo Liu, Nur Muhammad Mahi Shafiullah, Maya Guru, Ainaz Eftekhar, Karen Farley, Donovan Clay, Jiafei Duan, Arjun Guru, Piper Wolters, Alvaro Herrasti, Ying-Chun Lee, Georgia Chalvatzaki, Yuchen Cui, Ali Farhadi, Dieter Fox, Ranjay Krishna

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, ρ = 0.98), confirm newer and stronger 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.

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Bibtex:

  
@INPROCEEDINGS{KimY-RSS-26, 
    AUTHOR    = {Yejin Kim AND Wilbert Pumacay AND Omar Rayyan AND Max Argus AND Winson Han AND Eli Vanderbilt AND Jordi Salvador AND Abhay Deshpande AND Rose Hendrix AND Snehal Jauhri AND Shuo Liu AND Nur Muhammad Mahi Shafiullah AND Maya Guru AND Ainaz Eftekhar AND Karen Farley AND Donovan Clay AND Jiafei Duan AND Arjun Guru AND Piper Wolters AND Alvaro Herrasti AND Ying-Chun Lee AND Georgia Chalvatzaki AND Yuchen Cui AND Ali Farhadi AND Dieter Fox AND Ranjay Krishna}, 
    TITLE     = {{MolmoSpaces: Large-Scale Open Ecosystem for Robot Manipulation and Navigation}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.091} 
}