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Achieving grasp synthesis in less than five evaluations while maintaining state-of-the-art performance could revolutionize real-time robotic manipulation.
Allocating additional computation during decision-making boosts long-horizon robot manipulation success by significantly enhancing subtask prediction accuracy.
Neural motion planners can now navigate complex, multi-room environments with significantly improved robustness by incorporating sparse roadmap guidance as weak supervision.
