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arXiv · 2512.18933

Point What You Mean: Visually Grounded Instruction Policy

Hang Yu·Juntu Zhao·Yufeng Liu·Kaiyu Li·Cheng Ma·Di Zhang·Yingdong Hu·Guang Chen·Junyuan Xie·Junliang Guo·Junqiao Zhao·Yang Gao
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Abstract

Vision-Language-Action (VLA) models align vision and language with embodied control, but their object referring ability remains limited when relying solely on text prompt, especially in cluttered or out-of-distribution (OOD) scenes. In this study, we introduce the Point-VLA, a plug-and-play policy that augments language instructions with explicit visual cues (e.g., bounding boxes) to resolve referential ambiguity and enable precise object-level grounding. To efficiently scale visually grounded datasets, we further develop an automatic data annotation pipeline requiring minimal human effort. We evaluate Point-VLA on diverse real-world referring tasks and observe consistently stronger performance than text-only instruction VLAs, particularly in cluttered or unseen-object scenarios, with robust generalization. These results demonstrate that Point-VLA effectively resolves object referring ambiguity through pixel-level visual grounding, achieving more generalizable embodied control.

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Hang Yu, Juntu Zhao, Yufeng Liu, Kaiyu Li, Cheng Ma, Di Zhang, Yingdong Hu, Guang Chen, Junyuan Xie, Junliang Guo, Junqiao Zhao, Yang Gao. 2025-12-22. Point What You Mean: Visually Grounded Instruction Policy. https://arxiv.org/abs/2512.18933

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