Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
arXiv:2602.13810v1 Announce Type: new Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling complex action distributions with a fast deterministic sampling process, they still face a trade-off between expressiveness and computational burden, which is typically controlled by the number of flow steps. In this work, we propose mean velocity policy (MVP), a new generative policy function that models the mean velocity field to achieve the fastest one-step action generation. To ensure its high expressiveness, an instantaneous velocity constraint (IVC) is introduced on the mean velocity field during training. We theoretically prove that this design explicitly serves as a crucial boundary condition, thereby improving learning accuracy and enhancing policy expressiveness. Empirically, our MVP achieves state-of-the-art success rates across several challenging robo
arXiv:2602.13810v1 Announce Type: new Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling complex action distributions with a fast deterministic sampling process, they still face a trade-off between expressiveness and computational burden, which is typically controlled by the number of flow steps. In this work, we propose mean velocity policy (MVP), a new generative policy function that models the mean velocity field to achieve the fastest one-step action generation. To ensure its high expressiveness, an instantaneous velocity constraint (IVC) is introduced on the mean velocity field during training. We theoretically prove that this design explicitly serves as a crucial boundary condition, thereby improving learning accuracy and enhancing policy expressiveness. Empirically, our MVP achieves state-of-the-art success rates across several challenging robotic manipulation tasks from Robomimic and OGBench. It also delivers substantial improvements in training and inference speed over existing flow-based policy baselines.
Executive Summary
The article 'Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation' introduces a novel generative policy function called Mean Velocity Policy (MVP) for reinforcement learning (RL). MVP aims to model the mean velocity field to achieve rapid one-step action generation while maintaining high expressiveness. The authors introduce an instantaneous velocity constraint (IVC) during training to enhance learning accuracy and policy expressiveness. Theoretical proofs support the effectiveness of IVC as a boundary condition. Empirical results demonstrate MVP's superior performance in terms of success rates and computational efficiency compared to existing flow-based policy baselines across various robotic manipulation tasks.
Key Points
- ▸ MVP models the mean velocity field for fast one-step action generation.
- ▸ IVC is introduced to improve learning accuracy and policy expressiveness.
- ▸ Theoretical proofs support the effectiveness of IVC as a boundary condition.
- ▸ MVP achieves state-of-the-art success rates and computational efficiency in robotic manipulation tasks.
Merits
Innovative Approach
The MVP approach is innovative in its use of mean velocity field modeling for rapid action generation, addressing a critical need in RL for efficient and expressive policy functions.
Theoretical Rigor
The article provides theoretical proofs that support the effectiveness of the IVC, adding credibility to the proposed method.
Empirical Success
MVP demonstrates significant improvements in success rates and computational efficiency over existing flow-based policies in challenging robotic tasks.
Demerits
Limited Generalizability
The empirical results are primarily focused on robotic manipulation tasks, which may limit the generalizability of the findings to other RL domains.
Complexity of Implementation
The introduction of IVC adds complexity to the training process, which might require additional computational resources and expertise to implement effectively.
Expert Commentary
The article presents a significant advancement in the field of reinforcement learning by introducing the Mean Velocity Policy (MVP) with an Instantaneous Velocity Constraint (IVC). The innovative approach of modeling the mean velocity field for one-step action generation addresses a critical need for efficiency and expressiveness in policy functions. The theoretical proofs provided add a layer of rigor and credibility to the proposed method, ensuring that the IVC serves as an effective boundary condition. Empirical results across challenging robotic manipulation tasks further validate the superiority of MVP over existing flow-based policies. However, the focus on robotic tasks may limit the generalizability of the findings, and the added complexity of IVC implementation could pose challenges for broader adoption. Despite these limitations, the article's contributions are substantial and could influence both practical applications and policy development in AI research.
Recommendations
- ✓ Further research should explore the applicability of MVP in diverse RL domains beyond robotic manipulation to assess its generalizability.
- ✓ Future studies could investigate simplifying the implementation of IVC to make MVP more accessible and practical for a wider range of applications.
