MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation

• URL: http://arxiv.org/abs/2603.16861v2
• Date: Thu, 26 Mar 2026 17:59:03 GMT
• Title: MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
• Abstract summary: We show that zero-shot transfer to the real world is possible, but effective for both static and mobile manipulation.<n>We release MolmoBot-Data, a dataset of 1.8 million expert trajectories for articulated object manipulation and pick-and-place tasks.<n>Our results demonstrate that procedural environment generation combined with diverse articulated assets can produce robust manipulation policies.
• Score: 56.976138997544005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 website: https://allenai.github.io/MolmoBot

Related papers

• Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models [95.75234389806654]
  We present Qwen-RobotManip, a generalizable Vision-Language-Action foundation model built on Qwen-VL.<n>Qwen-RobotManip introduces a unified alignment framework across the representation, motion, and behavioral dimensions of manipulation.<n>A human-to-robot synthesis pipeline converts egocentric hand demonstrations into robot trajectories across 15 platforms.
  arXiv  Detail & Related papers  (2026-06-16T12:14:39Z)
• OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
  OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
  arXiv  Detail & Related papers  (2026-06-07T10:01:53Z)
• GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation [18.684526752120412]
  GSWorld is a photo-realistic simulator for robotics manipulation that combines 3D Gaussian Splatting with physics engines.<n>Our framework advocates "closing the loop" of developing manipulation policies with reproducible evaluation of policies learned from real-robot data.
  arXiv  Detail & Related papers  (2025-10-23T17:59:26Z)
• RoboPearls: Editable Video Simulation for Robot Manipulation [81.18434338506621]
  RoboPearls is an editable video simulation framework for robotic manipulation.<n>Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations.<n>We conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot.
  arXiv  Detail & Related papers  (2025-06-28T05:03:31Z)
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation [52.2244588424002]
  We present RoboTwin 2.0, a scalable framework for automated, large-scale generation of diverse and realistic data.<n>At its core is RoboTwin-OD, an object library of 731 instances across 147 categories with semantic and manipulation-relevant annotations.<n>To improve sim-to-real transfer, RoboTwin 2.0 applies structured domain randomization along five axes.
  arXiv  Detail & Related papers  (2025-06-22T16:26:53Z)
• ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis [39.50916343607966]
  ReBot is a novel real-to-sim-to-real approach for scaling real robot datasets.<n>We show that ReBot significantly enhances the performance and robustness of vision-language-action (VLA) models.
  arXiv  Detail & Related papers  (2025-03-15T16:47:25Z)
• Manipulate-Anything: Automating Real-World Robots using Vision-Language Models [47.16659229389889]
  We propose Manipulate-Anything, a scalable automated generation method for real-world robotic manipulation. Manipulate-Anything can operate in real-world environments without any privileged state information, hand-designed skills, and can manipulate any static object.
  arXiv  Detail & Related papers  (2024-06-27T06:12:01Z)
• M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place [44.303123422422246]
  M2T2 is a single model that supplies different types of low-level actions that work robustly on arbitrary objects in cluttered scenes. M2T2 is trained on a large-scale synthetic dataset with 128K scenes and achieves zero-shot sim2real transfer on the real robot.
  arXiv  Detail & Related papers  (2023-11-02T01:42:52Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• Deep Imitation Learning for Bimanual Robotic Manipulation [70.56142804957187]
  We present a deep imitation learning framework for robotic bimanual manipulation. A core challenge is to generalize the manipulation skills to objects in different locations. We propose to (i) decompose the multi-modal dynamics into elemental movement primitives, (ii) parameterize each primitive using a recurrent graph neural network to capture interactions, and (iii) integrate a high-level planner that composes primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
  arXiv  Detail & Related papers  (2020-10-11T01:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.