AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning

Published July 20, 2026 · Category: Robotics

Overview

arXiv:2607.15714v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models excel at end-to-end robotic manipulation but struggle with out-of-distribution (OOD) generalization when familiar sub-tasks are recombined in unseen configurations. We identify two mutually reinforcing failure modes: \emph{trajectory overfitting}, where models overfit to holistic trajectory patterns rather than compositional sub-skill semantics; and \emph{perceptual shortcut}, where action tokens over-rely on wrist-view textures at the expense of global spatial grounding. To address both, we introduce \textbf{AC-VLA}, a plug-and-play Action Compositional learning framework comprising two architecture-agnostic components: \textbf{(i)} a compositional learning module that uses an LLM-driven instruction decomposer and a proprioceptive trajectory aligner to generate dense sub-task supervision, followed by mixed training on complete demonstrations and decomposed data to endow the model with compositional generalization; and \textbf{(ii)} a state-conditioned asymmetric masking strategy that suppresses wrist-view inputs during closed-gripper phases, enforcing global semantic grounding. All components are architectural modification-free and directly integrable into any VLA backbone. Instantiated on $\pi_{0.5}$ and evaluated on LIBERO and LIBERO-OOD benchmarks, AC-VLA achieves a ~28% absolute improvement on compositional OOD tasks while maintaining near-perfect in-distribution performance.

Source

Originally published at arxiv.org.

Related Articles

• A Task-Space Receding Horizon Controller for Fast Collision Avoidance
• Learning a System-Level Surrogate for Hydraulic Excavators: A Simulation-to-Real LSTM Approach
• Passive Phase-Oriented Impedance Shaping for Rapid Acceleration in Soft Robotic Swimmers