@inproceedings{yu-etal-2026-robosemanticbench,
title = "{R}obo{S}emantic{B}ench: Diagnosing Semantic Grounding in Action Prediction for {VLA} Models",
author = "Yu, Bin and
Zhang, Yao and
Liu, Haishan and
Lian, Shijie and
Wei, Yuliang and
Lin, Xiaopeng and
Shen, Zhaolong and
Wu, Changti and
Hu, Ruina and
Wang, Bailing and
Huang, Cong and
Chen, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.199/",
pages = "4209--4223",
ISBN = "979-8-89176-462-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models
%A Yu, Bin
%A Zhang, Yao
%A Liu, Haishan
%A Lian, Shijie
%A Wei, Yuliang
%A Lin, Xiaopeng
%A Shen, Zhaolong
%A Wu, Changti
%A Hu, Ruina
%A Wang, Bailing
%A Huang, Cong
%A Chen, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yu-etal-2026-robosemanticbench
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.199/
%P 4209-4223
Markdown (Informal)
[RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models](https://aclanthology.org/2026.findings-emnlp.199/) (Yu et al., Findings 2026)
ACL
- Bin Yu, Yao Zhang, Haishan Liu, Shijie Lian, Yuliang Wei, Xiaopeng Lin, Zhaolong Shen, Changti Wu, Ruina Hu, Bailing Wang, Cong Huang, and Kai Chen. 2026. RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4209–4223, Budapest, Hungary. Association for Computational Linguistics.