MolmoSpaces: 一个大规模的开放式生态系统用于机器人导航与操作
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
AI总结：
MolmoSpaces是一个大规模开放式生态系统，用于支持机器人导航与操作的大规模基准测试，包含230k多样化的室内环境和130k丰富标注的物体资产，通过8个任务的基准测试验证了其在仿真到现实中的强相关性。
摘要：
在大规模部署机器人时，需要具备对日常情况长尾的鲁棒性。真实环境中场景布局、物体几何和任务规范的无数变化是庞大且在现有机器人基准中代表性不足的。衡量这种级别的泛化能力需要基础设施的规模和多样性，这单靠物理评估本身无法提供。我们介绍了MolmoSpaces，一个完全开放的生态系统，用于支持机器人策略的大规模基准测试。MolmoSpaces包含超过230,000个多样化的室内环境，从手工制作的家居场景到程序生成的多房间房屋，这些环境中充满了130,000个丰富标注的物体资产，包括48,000个可操作的物体，拥有4200万种稳定的抓取方式。关键的是，这些环境是模拟器无关的，支持流行的选项如MuJoCo、Isaac和ManiSkill。该生态系统支持从静态和移动操作、导航到多房间长周期任务的整个具身任务，这些任务需要在整个室内环境中协调感知、规划和交互。我们还设计了MolmoSpaces-Bench，一个包含8个任务的基准测试集，其中机器人与我们的多样化场景和丰富标注的物体互动。我们的实验表明，MolmoSpaces-Bench表现出强大的仿真到现实相关性（R=0.96，ρ=0.98），确认了最新且更强的零样本策略在我们的基准测试中优于早期版本，并识别了对提示措辞、初始关节位置和摄像头遮挡的关键敏感性。通过MolmoSpaces及其开源资产和工具，我们为机器人学习研究的可扩展数据生成、策略训练和基准创建提供了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
