Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancement is constrained by the high cost of acquiring large-scale interaction datasets. This work introduces an alternative paradigm for enhancing policy performance without additional model training. Perhaps surprisingly, we demonstrate that the composed policies can exceed the performance of either parent policy. Our contribution is threefold. First, we establish a theoretical foundation showing that the convex composition of distributional scores from multiple diffusion models can yield a superior one-step functional objective compared to any individual score. A Grönwall-type bound is then used to show that this single-step improvement propagates through entire generation trajectories, leading to systemic performance gains. Second, motivated by these results, we propose General Policy Composition (GPC), a training-free method that enhances performance by combining the distributional scores of multiple pre-trained policies via a convex combination and test-time search. GPC is versatile, allowing for the plug-and-play composition of heterogeneous policies, including VA and VLA models, as well as those based on diffusion or flow-matching, irrespective of their input visual modalities. Third, we provide extensive empirical validation. Experiments on Robomimic, PushT, and RoboTwin benchmarks, alongside real-world robotic evaluations, confirm that GPC consistently improves performance and adaptability across a diverse set of tasks. Further analysis of alternative composition operators and weighting strategies offers insights into the mechanisms underlying the success of GPC. These results establish GPC as a simple yet effective method for improving control performance by leveraging existing policies.

翻译：基于扩散的机器人控制模型，包括视觉-语言-动作（VLA）和视觉-动作（VA）策略，已展现出显著能力。然而，其发展受限于获取大规模交互数据集的高昂成本。本文提出了一种无需额外模型训练即可提升策略性能的替代范式。或许令人惊讶的是，我们证明了组合策略的性能可以超越任一父策略。我们的贡献有三方面。首先，我们建立了理论基础，表明多个扩散模型分布分数的凸组合可以产生优于任何单个分数的一步函数目标。随后使用格朗沃尔型界证明，这种单步改进会传播至整个生成轨迹，从而实现系统性性能提升。其次，受这些结果启发，我们提出了通用策略组合（GPC），这是一种无需训练的方法，通过凸组合和测试时搜索结合多个预训练策略的分布分数来提升性能。GPC具有通用性，支持异构策略的即插即用式组合，包括VA和VLA模型，以及基于扩散或流匹配的模型，且不受其输入视觉模态的限制。第三，我们提供了广泛的实证验证。在Robomimic、PushT和RoboTwin基准测试以及真实世界机器人评估中的实验证实，GPC能持续提升多样化任务集的性能和适应性。对替代组合算子和权重策略的进一步分析，为理解GPC成功的机制提供了见解。这些结果确立了GPC作为一种通过利用现有策略来提升控制性能的简单而有效的方法。