StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle with precise spatial reasoning, particularly in predicting object locations and fine-grained object states. We propose StateVLM, a vision-language model designed to learn fine-grained object representations, including object localization and grasp-relevant region prediction. We introduce a joint training objective that integrates an auxiliary regression loss (ARL) with the standard causal language modeling (CLM) objective to improve numerical reasoning and spatial understanding. To evaluate whether models can move beyond category-level groundi...
| Authors | Xiaowen Sun, Matthias Kerzel, Mengdi Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 9 September 2026 |
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