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Policies trained on RynnWorld-Teleop's synthetic data achieve zero-shot transfer to real-world tasks, revolutionizing how we collect and utilize robotic training data.
RynnWorld-4D transforms robotic manipulation by co-producing future scene dynamics from a single RGB-D image, leading to unprecedented performance in dexterous tasks.
RoboDojo reveals that existing benchmarks fail to capture the full spectrum of robot manipulation capabilities, paving the way for more robust evaluations that bridge the gap between simulation and real-world performance.
