Vision-language-action (VLA) models are expected to follow instructions like their language backbones, yet prior work has focused on What-level instructions that specify the task rather than How-level...
arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.
By Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy
arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.
By Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen
arXiv:2610.10526v1 Announce Type: cross
Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-langua...
By Mikey Watts (Independent Researcher), Yuchen Cui (University of California, Los Angeles)
DeicticVLA unifies three instruction modes—Language Instruction, Vision‑Language Instruction, and Visual Instruction—into a single text prompt and deictic mask framework, allowing a single pretrained Vision‑Language‑Action model to handle all modes. The approach uses text‑prompt completion and deictic gesture grounding, and evaluates various visual prompting methods and training strategies in simulation and real‑world tasks. Results show that two‑stage training improves deictic mask usage, and that Vision‑Language and Visual Instruction outperform Language Instruction on unseen expressions, appearance changes, and novel objects, achieving 100% success on unseen categories.
By Kango Yanagida, Tatsuya Aoki, Yuichiro Yoshikawa, Takato Horii
The paper investigates a specific failure mode in vision‑language‑action models, termed instruction‑action binding, where models respond to language and vision separately but fail to combine them to select the correct action under counterfactual changes. Through behavioral analyses of fine‑tuned policies, the authors show that failed rollouts often preserve source behavior or switch to other demonstrated tasks, indicating that language is not ignored but mis‑bound. They propose Equivariant Counterfactual Training (ECT), which supplies counterfactual demonstrations and a paired loss to enforce correct action selection, achieving significant performance gains across simulated and real‑world benchmarks.
By Hung-Jen Chen, Yu-Hsun Hou, Yan-Hong Chen, Yan-Fu Chen, Binghua Cai, Min Sun, Chun-Yi Lee