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Task-aligned simulated futures can dramatically improve robot policy training, especially for complex, long-horizon tasks.
Control over physical properties like friction and restitution in generated videos is now possible, paving the way for more realistic and controllable video synthesis.
Real-time autonomous driving with language models is now possible, achieving 3x speedup and state-of-the-art performance by combining learned and rule-based planning.
