Robots deployed in unstructured environments must coordinate whole- body motion—simultaneously moving a mobile base and arm—to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed- base manipulation. Yet existing acquisition methods, including teleoperation [13] and planning [22, 38], are either labor- intensive or computationally prohibitive at scale. The core bottleneck is the lack of a scalable pipeline for generating large- scale, physically valid, coordinated trajectory data across diverse embodiments and environments. Here we introduce AutoMoMa, a GPU- accelerated framework that unifies AKR modeling, which consolidates base, arm, and object kinematics into a single chain, with parallelized trajectory optimization. AutoMoMa achieves 5,000 episodes per GPU- hour (over 80ˆ faster than CPU- based baselines [50]), producing a dataset of over 500k physically valid trajectories spanning 330 scenes, diverse articulated objects, and multiple robot embodiments. Prior datasets were forced to compromise on scale, diversity, or kinematic fidelity [13, 50]; AutoMoMa addresses all three simultaneously. Training downstream IL policies further reveals that even a single articulated- object task requires tens of thousands of demonstrations for State- of- the- Art (SOTA) methods to reach «80% success, confirming that data scarcity—not algorithmic limitations—has been the binding constraint. AutoMoMa thus bridges high- performance planning and reliable IL- based control, providing the in frastructure previously missing for coordinated mobile manipulation research. By making large- scale, kinematically valid training data practical, AutoMoMa showcases generalizable whole- body robot policies capable of operating in the diverse, unstructured settings of the real world.