MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
Authors: name: Abhay Deshpande, name: Maya Guru, name: Rose Hendrix, name: Snehal Jauhri, name: Ainaz Eftekhar, name: Rohun Tripathi, name: Max Argus, name: Jordi Salvador, name: Haoquan Fang, name: Matthew Wallingford, name: Wilbert Pumacay, name: Yejin Kim, name: Quinn Pfeifer, name: Ying-Chun Lee, name: Piper Wolters, name: Omar Rayyan, name: Mingtong Zhang, name: Jiafei Duan, name: Karen Farley, name: Winson Han, name: Eli Vanderbilt, name: Dieter Fox, name: Ali Farhadi, name: Georgia Chalvatzaki, name: Dhruv Shah, name: Ranjay Krishna
Organization: Ai2

A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific fine-tuning to bridge the gap between simulated and physical environments. We challenge that assumption. With sufficiently large-scale and diverse simulated synthetic training data, we show that zero-shot transfer to the real world is not only possible, but effective for both static and mobile manipulation. We introduce MolmoBot-Engine, a fully open-source pipeline for procedural data generation across robots, tasks, and diverse simulated environments in MolmoSpaces. With it, we release MolmoBot-Data, a dataset of 1.8 million expert trajectories for articulated object manipulation and pick-and-place tasks. We train three policy classes: MolmoBot, a Molmo2-based multi-frame vision-language model with a flow-matching action head; MolmoBot-Pi0, which replicates the π_0 architecture to enable direct comparison; and MolmoBot-SPOC, a lightweight policy suitable for edge deployment and amenable to RL fine-tuning. We evaluate on two robotic platforms: the Franka FR3 for tabletop manipulation tasks and the Rainbow Robotics RB-Y1 mobile manipulator for door opening, drawer manipulation, cabinet interaction, and mobile pick-and-place. Without any real-world fine-tuning, our policies achieve zero-shot transfer to unseen objects and environments. On tabletop pick-and-place, MolmoBot achieves a success rate of 79.2% in real world evaluations across 4 settings, outperforming π_{0.5} at 39.2%. Our results demonstrate that procedural environment generation combined with diverse articulated assets can produce robust manipulation policies that generalize broadly to the real world. Technical Blog: https://allenai.org/blog/molmobot-robot-manipulation