ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training

Abstract

Generative models based on flow matching offer significant potential for learning robot policies, particularly in generating high-dimensional, dexterous behaviors that are conditioned on diverse observations. In this work, we introduce ManiFlow, an advanced flow matching model specifically designed to support dexterous manipulation tasks. ManiFlow improves over flow matching both in the learning procedure and in the model architecture, resulting in better robustness and efficacy. It consistently exhibits strong generalization capabilities, outperforming existing state-of-the-art robot learning methods on a wide range of benchmarks. We also demonstrate the powerful capabilities of ManiFlow in solving complex bimanual dexterous manipulation challenges.