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A novel bidirectional curriculum strategy unlocks efficient exploration in sparse-reward RL, outperforming unidirectional methods and even matching demonstration-based approaches on challenging tasks.
The SE(2) NavMesh captures over 50% more traversable area than classical navigation meshes, revolutionizing navigation for non-circular robots in complex environments.
Even near-perfect planning ability in LLMs doesn't ensure safety in robotic tasks, with the best models still generating dangerous plans almost 30% of the time.
