Compose Your Policies! Improving Diffusion-based or Flow-based Robot
  Policies via Test-time Distribution-level Composition
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
Gr\"onwall-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.
68de0d466024653e8a3ed16f
Jiahang Cao
claimed_verified
2025-10-06T03:21:22.930Z
64780ba6f32a4117fd182b81
/avatars/85f01f4c6c745a04f04805462f9fe9c2.svg
CAO
user
SAGE2000
68de0d466024653e8a3ed170
Yize Huang
68de0d466024653e8a3ed171
Hanzhong Guo
68de0d466024653e8a3ed172
Rui Zhang
68de0d466024653e8a3ed173
Mu Nan
68de0d466024653e8a3ed174
Weijian Mai
68de0d466024653e8a3ed175
Jiaxu Wang
68de0d466024653e8a3ed176
Hao Cheng
68de0d466024653e8a3ed177
Jingkai Sun
68de0d466024653e8a3ed178
Gang Han
68de0d466024653e8a3ed179
Wen Zhao
68de0d466024653e8a3ed17a
Qiang Zhang
68de0d466024653e8a3ed17b
Yijie Guo
68de0d466024653e8a3ed17c
Qihao Zheng
68de0d466024653e8a3ed17d
Chunfeng Song
68de0d466024653e8a3ed17e
Xiao Li
68de0d466024653e8a3ed17f
Ping Luo
68de0d466024653e8a3ed180
Andrew F. Luo
claimed_verified
2025-10-06T03:21:20.592Z
64b6ce23dbbd1f2cdb624d56
/avatars/022738ce8f76fa9545b3e363d7264b53.svg
Andrew Luo
user
aluo-x
Compose Your Policies! Improving Diffusion-based or Flow-based Robot
  Policies via Test-time Distribution-level Composition
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
Gr\"onwall-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.
General Policy Composition (GPC) enhances robotic control performance by combining pre-trained diffusion-based policies without additional training, leading to superior results across various benchmarks.
diffusion-based models vision-language-action vision-action distributional scores convex composition Gr\"onwall-type bound General Policy Composition GPC pre-trained policies Robomimic PushT RoboTwin real-world robotic evaluations