Differentiable Contact Unlocks Real-World Locomotion Control

By smoothing hard contact dynamics inside a fully differentiable simulation, robust locomotion controllers can be learned efficiently without sacrificing physical fidelity. The resulting policies generate stable gaits, track velocity commands, and resist disturbances—while transferring zero-shot from simulation to real robots. The approach shows how contact-aware, gradient-based learning can produce deployable motion control for legged systems.