ACG: Action Coherence Guidance for Flow-based VLA models
Diffusion and flow matching models have emerged as powerful robot policies,
enabling Vision-Language-Action (VLA) models to generalize across diverse
scenes and instructions. Yet, when trained via imitation learning, their high
generative capacity makes them sensitive to noise in human demonstrations:
jerks, pauses, and jitter which reduce action coherence. Reduced action
coherence causes instability and trajectory drift during deployment, failures
that are catastrophic in fine-grained manipulation where precision is crucial.
In this paper, we present Action Coherence Guidance (ACG) for VLA models, a
training-free test-time guidance algorithm that improves action coherence and
thereby yields performance gains. Evaluated on RoboCasa, DexMimicGen, and
real-world SO-101 tasks, ACG consistently improves action coherence and boosts
success rates across diverse manipulation tasks. Code and project page are
available at https://github.com/DAVIAN-Robotics/ACG and
https://DAVIAN-Robotics.github.io/ACG , respectively.
69002ae322d452aac6dd42aa
Minho Park
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2025-10-28T15:36:49.518Z
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Minho Park
mpark
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mpark
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Kinam Kim
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2025-10-28T15:36:46.749Z
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Kinam Kim
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user
kinam0252
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Junha Hyung
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Hyojin Jang
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Hoiyeong Jin
claimed_verified
2025-12-25T20:54:40.704Z
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Hoiyeong Jin
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user
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Jooyeol Yun
claimed_verified
2025-12-29T14:25:59.086Z
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/avatars/e937dc8234b3e456149882bfce34841f.svg
Jooyeol Yun
YeolJoo
user
YeolJoo
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Hojoon Lee
69002ae322d452aac6dd42b1
Jaegul Choo
ACG: Action Coherence Guidance for Flow-based VLA models
Diffusion and flow matching models have emerged as powerful robot policies,
enabling Vision-Language-Action (VLA) models to generalize across diverse
scenes and instructions. Yet, when trained via imitation learning, their high
generative capacity makes them sensitive to noise in human demonstrations:
jerks, pauses, and jitter which reduce action coherence. Reduced action
coherence causes instability and trajectory drift during deployment, failures
that are catastrophic in fine-grained manipulation where precision is crucial.
In this paper, we present Action Coherence Guidance (ACG) for VLA models, a
training-free test-time guidance algorithm that improves action coherence and
thereby yields performance gains. Evaluated on RoboCasa, DexMimicGen, and
real-world SO-101 tasks, ACG consistently improves action coherence and boosts
success rates across diverse manipulation tasks. Code and project page are
available at https://github.com/DAVIAN-Robotics/ACG and
https://DAVIAN-Robotics.github.io/ACG , respectively.
Action Coherence Guidance (ACG) improves action coherence in Vision-Language-Action (VLA) models during test time, enhancing performance in diverse manipulation tasks.
diffusion models flow matching models Vision-Language-Action (VLA) models imitation learning action coherence trajectory drift RoboCasa DexMimicGen SO-101 tasks Action Coherence Guidance (ACG)