Deep dives on simulation-ready 3D data, domain randomization, world models and sim-to-real transfer: the research behind training embodied AI at scale.
Objaverse is a library of shapes, not simulation assets. Why they fail in a physics engine, and what Physicl provides instead. Request access
LiDAR simulation returns believable, wrong point clouds when 3D assets carry no material data. Physicl ships physics-tagged assets. Request access
Simulation-ready 3D assets need more than geometry: physics properties, correct collision meshes, USD compatibility and verified behavior in the simulator.
Training world models on 3D spatial data has a problem most teams don’t surface until it’s too late: IP licensing. Here’s why provenance matters as much as scale.
Physical AI training data is physics-accurate 3D data used to train robots and embodied AI. What it is, why it's hard to produce, and what sim-ready means.
