RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models
Authors: name: Bin Yu, name: Yao Zhang, name: Haishan Liu, name: Shijie Lian, name: Yuliang Wei, name: Xiaopeng Lin, name: Zhaolong Shen, name: Changti Wu, name: Ruina Hu, name: Bailing Wang, name: Cong Huang, name: Kai Chen
Organization: Zhongguancun Academy

Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction. Yet robot fine-tuning is optimized as imitation over task-specific action distributions, and many evaluations can be solved through visual or instruction-action shortcuts. We introduce RoboSemanticBench (RSB), an embodied benchmark for diagnosing semantic grounding in action prediction: whether post-trained VLA models can use complex instruction semantics to select and manipulate the correct physical target. In each episode, a robot receives a multiple-choice math or general-knowledge question, observes candidate answer blocks, and must grasp the block corresponding to the correct answer. RSB covers controlled arithmetic, grade-school mathematical understanding, and commonsense or factual understanding under four-choice and ten-choice suites. Across representative VLA models, we find that many policies learn to grasp candidate blocks but select the semantically correct block at near-random or below-random rates after controlling for grasp success, revealing a persistent gap between backbone-level semantic competence and action prediction.