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This work proposes DIDO, which distills the converged dynamics of a multi-step video model into a single denoising step and achieves an average success rate of 99.0\% on LIBERO, 76.6\% on LIBERO-Plus, and 92.0\% on RoboTwin, while also demonstrating effective transfer to long-horizon and generalization tasks in real-world robotic manipulation.
Fine-grained metrics reveal that robots can recover from failures more effectively than previously thought, reshaping our understanding of their capabilities.
Orca's unified world latent space enables superior performance in diverse tasks, outperforming specialized models with a single framework.
