MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
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, 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.
698e9615cace060ff123adc9
Yejin Kim
698e9615cace060ff123adca
Wilbert Pumacay
698e9615cace060ff123adcb
Omar Rayyan
698e9615cace060ff123adcc
Max Argus
698e9615cace060ff123adcd
Winson Han
698e9615cace060ff123adce
Eli VanderBilt
698e9615cace060ff123adcf
Jordi Salvador
698e9615cace060ff123add0
Abhay Deshpande
698e9615cace060ff123add1
Rose Hendrix
698e9615cace060ff123add2
Snehal Jauhri
698e9615cace060ff123add3
Shuo Liu
claimed_verified
2026-02-23T09:46:55.037Z
6687a6261c9173b89f325323
https://cdn-avatars.huggingface.co/v1/production/uploads/6687a6261c9173b89f325323/7GpTfSD30KPbg6gXIWLbv.jpeg
Shuo Liu
TreeePlanter
user
TreeePlanter
698e9615cace060ff123add4
Nur Muhammad Mahi Shafiullah
698e9615cace060ff123add5
Maya Guru
698e9615cace060ff123add6
Ainaz Eftekhar
698e9615cace060ff123add7
Karen Farley
698e9615cace060ff123add8
Donovan Clay
698e9615cace060ff123add9
Jiafei Duan
698e9615cace060ff123adda
Arjun Guru
698e9615cace060ff123addb
Piper Wolters
698e9615cace060ff123addc
Alvaro Herrasti
698e9615cace060ff123addd
Ying-Chun Lee
claimed_verified
2026-03-23T11:01:05.800Z
65df2bb4cec6b1667f120cad
https://cdn-avatars.huggingface.co/v1/production/uploads/65df2bb4cec6b1667f120cad/TWJJ6vXIYAUlcegHVlTJd.jpeg
Ying-Chun Lee
thomas0829
user
thomas0829
698e9615cace060ff123adde
Georgia Chalvatzaki
698e9615cace060ff123addf
Yuchen Cui
698e9615cace060ff123ade0
Ali Farhadi
698e9615cace060ff123ade1
Dieter Fox
698e9615cace060ff123ade2
Ranjay Krishna
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
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, 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.
MolmoSpaces presents an open ecosystem with diverse indoor environments and annotated objects for large-scale robot policy benchmarking across multiple tasks and simulators.
robot policies embodied tasks sim-to-real correlation zero-shot policies prompt phrasing initial joint positions camera occlusion