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论文arXiv 2606.022774 个月前

RoboSemanticBench基准揭示VLA模型语义接地与动作预测严重脱节 ​

新基准RSB通过数学与常识问答抓取任务诊断VLA模型，发现其在控制抓取成功率后，语义正确目标选择率接近或低于随机水平，暴露预训练语义理解未能有效转化为具身动作决策的关键瓶颈。

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本文由 AI 根据公开内容整理，可能有误，请以原文为准：RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models（arXiv 2606.02277）

核心内容 ​

针对VLA模型微调常退化为动作模仿、评测易受视觉捷径干扰的问题，研究提出RoboSemanticBench（RSB）具身基准。该基准要求机器人根据多选数学或常识问题，从候选方块中抓取对应正确答案的物体，涵盖算术、小学数学及常识事实理解，设置四选一与十选一两种难度套件，旨在定量诊断后训练VLA模型的语义接地能力。

实验结果显示，尽管代表性VLA模型能学会抓取候选方块，但在剔除抓取失败样本后，其选中语义正确目标的概率仍接近甚至低于随机基线。这表明模型虽继承了骨干网络的语义表征能力，却未能在动作预测阶段有效利用复杂指令语义进行物理目标选择，证实了当前VLA在语义理解到具身执行之间存在显著断层。

关键看点 ​

  • 评测机制：RSB采用多选问答驱动抓取范式，覆盖算术、数学理解及常识事实，设4/10选项难度梯度
  • 核心发现：控制抓取成功率后，VLA模型语义正确目标选择率仍接近或低于随机水平
  • 技术瓶颈：揭示预训练语义能力未有效迁移至动作预测，存在语义接地与执行策略断层
  • 基准价值：提供排除视觉/指令-动作捷径的纯语义接地诊断工具，量化VLA具身推理缺陷

工程与产业观察 ​

RSB揭示了当前VLA模型“懂语义但不会做”的工程痛点，提示算法研发需超越单纯的动作模仿学习。未来应强化语义符号到物理动作空间的显式对齐训练，或在推理链路中引入语义验证模块，避免端到端策略在复杂指令下退化为随机试探，这对提升具身智能在非结构化环境中的可靠性具有关键指导意义。

英文摘要 (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.

作者：Bin Yu, Yao Zhang, Haishan Liu, Shijie Lian, Yuliang Wei, Xiaopeng Lin, Zhaolong Shen, Changti Wu, Ruina Hu, Bailing Wang, Cong Huang, Kai Chen

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2606.02277：RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models访问 2026-10-23