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Achieving over 97% success in long-horizon robot manipulation, TemporalFlow-VLA reveals the critical role of execution history in action prediction.
Human video demonstrations can transform robot learning, enabling zero-shot generalization to unseen tasks with remarkable success rates.
MVUCF achieves a remarkable 98.9% on LIBERO, showcasing a 22.4-point improvement on LIBERO-Plus and a 23.3-point increase in task success rates, all without additional inference costs.
By jointly modeling video dynamics and actions, DiT4DiT achieves 10x sample efficiency and 7x faster convergence in robot policy learning, showing that video generation can be a powerful scaling proxy.
