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Fine-tuning Vision-Language Model planners for robotic manipulation is now significantly more efficient and safer thanks to a novel framework that leverages video world models to simulate real-world physics.
Forget hand-engineered reward functions: this work shows VLMs can provide reliable, zero-shot feedback for online robot policy refinement, boosting success rates on manipulation tasks in just 30 RL iterations.
Forget scaling laws: this humanoid robot model crushes benchmarks using 10x less data by cleverly pre-training on human videos and then fine-tuning on robot-specific movements.
