SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation

Large-scale, diverse datasets have driven the success of LLMs and VLMs. But VLAs for robotics remain limited by the cost and complexity of real-world data collection. While simulation offers a scalable alternative, its potential for zero-shot sim-to-real VLA learning in mobile manipulation remains largely underexplored. We introduce SimVLA, a framework that trains VLAs entirely from synthetic simulation data generated without teleoperation for mobile manipulation. SimVLA is first pre-trained on two complementary simulation-derived datasets: SimAction, a large-scale robot action dataset spanning 30 diverse mobile manipulation tasks, generated by composing atomic skills, and SimVQA, which leverages privileged simulator state to provide spatial, geometric, and subtask-level visual-language supervision. We further post-train SimVLA on a mixture of SimAction and SimDeploy, a dataset collected from policy rollouts across diverse simulated environments. We evaluate SimVLA on tasks including restocking, pouring, and cleaning, and show zero-shot transfer to real-world mobile manipulation, including real home environments. SimVLA outperforms policies trained on 50 in-domain real-world demonstrations, suggesting that simulation can enable scalable zero-shot sim-to-real mobile manipulation. We also demonstrate that multiple complementary forms of supervision in simulation can serve as an compelling alternative to expensive real-world data collection.