ACG: Action Coherence Guidance for Flow-based VLA models
Authors: name: Minho Park, name: Kinam Kim, name: Junha Hyung, name: Hyojin Jang, name: Hoiyeong Jin, name: Jooyeol Yun, name: Hojoon Lee, name: Jaegul Choo
Organization: DAVIAN Robotics

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.