Scalable Trajectory Generation for Whole-Body Mobile Manipulation

Mobile robots need coordinated whole-body motion to perform household tasks effectively. Current mobile manipulation datasets rely on expensive teleoperation or slow planning methods, limiting available data to hundreds of demonstrations. This data scarcity severely constrains the development of generalizable learning-based policies. Here, we demonstrate that GPU-accelerated planning generates up to 5,000 episodes per GPU hour, over 80 $\times$ faster than existing methods. Our AutoMoMa pipeline produces 500K diverse physically valid whole-body motions across 300 household scenes and multiple robot embodiments, compared to previous datasets limited to narrow robot-scene pairs with a few hundred demonstrations. Downstream validation demonstrates consistent policy improvements with large-scale training data. This work provides the first scalable solution to the mobile manipulation data bottleneck. By enabling massive dataset generation, AutoMoMa accelerates progress toward general-purpose household robots capable of complex coordination tasks.