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Lagrangian Neural Networks embedded within an RL MPC framework enable quadrupedal robots to achieve 4x faster planning with minimal performance loss by avoiding costly matrix inversions.
Legged robots can now nimbly navigate complex terrains by fusing learned visual perception with infinite-horizon planning, achieving robustness beyond traditional MPC or RL approaches.
