Zero-shot sim-to-real transfer for complex robotic manipulation is achievable using only synthetic simulated data at scale.
March 18, 2026
Original Paper
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
arXiv · 2603.16861
The Takeaway
It challenges the consensus that real-world data or fine-tuning is required for effective robotic deployment. By releasing a pipeline for 1.8M expert trajectories and demonstrating zero-shot success on diverse platforms, it provides a blueprint for scaling robotics through simulation alone.
From the 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 manipulati