SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment

The introduction of the SeFA-Policy framework marks a significant advancement in robotic imitation learning, particularly in the context of visuomotor policy development. Traditional methods often struggled with maintaining accuracy during iterative action generation, leading to errors that compounded over time. SeFA addresses this challenge through a selective flow alignment strategy that leverages expert demonstrations to correct generated actions, ensuring they align with current visual observations. This innovative approach not only preserves the multimodality of actions but also introduces a consistency correction mechanism that enhances the reliability of task execution. Extensive experiments demonstrate that SeFA outperforms existing diffusion-based and flow-based policies, achieving over 98% reduction in inference latency. This improvement is crucial for real-time applications in robotics, where efficiency and accuracy are paramount. The implications of this research extend bey…