AI RESEARCH
Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning
arXiv CS.LG
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ArXi:2604.08780v1 Announce Type: cross World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning and behavior learning. However, current world models are often hardware-locked specialists: a model trained on a Boston Dynamics Spot robot fails catastrophically on a Unitree Go1 due to the mismatch in kinematic and dynamic properties, as the model overfits to specific embodiment constraints rather than capturing the universal locomotion dynamics.