Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding

Understand

Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-Language Models (VLMs) for action prediction.

• Yet most VLAs entangle perception and control in a monolithic pipeline optimized purely for action, which can erode language-conditioned grounding.
• In our real-world tabletop tests, policies over-grasp when the target is absent, are distracted by clutter, and overfit to background appearance.
• To address these issues, we propose OBEYED-VLA (OBject-centric and gEometrY groundED VLA), a framework that explicitly disentangles perceptual grounding from action reasoning.