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This paper introduces the Mean Velocity Policy (MVP), a new generative policy function in reinforcement learning that models the mean velocity field for efficient one-step action generation. The MVP incorporates an Instantaneous Velocity Constraint (IVC) to enhance learning accuracy and expressiveness, achieving state-of-the-art performance on robotic manipulation tasks while significantly improving training and inference speed. The proposed method addresses the limitations of existing flow-based policies b

Author

tongcheprivate

Language

EN