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[2602.11337] MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Summary
MolmoSpaces introduces a large-scale open ecosystem designed for benchmarking robot navigation and manipulation, featuring over 230k diverse indoor environments and 130k annotated object assets.
Why It Matters
This research addresses the limitations of existing robot benchmarks by providing a comprehensive platform that supports diverse and realistic scenarios for robot learning. It enhances the ability to evaluate and improve robot policies in various tasks, ultimately contributing to advancements in robotics and AI applications.
Key Takeaways
- MolmoSpaces includes 230k diverse indoor environments for benchmarking.
- The ecosystem supports various simulators like MuJoCo and Isaac.
- It features a benchmark suite with strong sim-to-real correlation.
- The platform aids in scalable data generation and policy training.
- Key insights reveal sensitivities in robot interactions and task performance.
Computer Science > Robotics arXiv:2602.11337 (cs) [Submitted on 11 Feb 2026 (v1), last revised 19 Feb 2026 (this version, v2)] Title:MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation Authors: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 View a PDF of the paper titled MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation, by Yejin Kim and 25 other authors View PDF HTML (experimental) 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, inclu...