arXiv:2606.15714v1 Announce Type: new Abstract: Vision-Language-Action models have recently demonstrated promising capabilities in learning generalist robot policies from large-scale multimodal data. However, most existing VLA systems are trained and evaluated primarily with English instructions, leaving their ability to understand and execute instructions in other languages largely unexplored. While the underlying large language models often possess multilingual capabilities, it remains unclear whether these multilingual capabilities transfer to VLAs during training. In this work, we present
The proliferation of Vision-Language-Action (VLA) models has exposed a critical limitation in their real-world applicability outside of English instruction sets, prompting immediate research into multilingual capabilities.
A significant portion of global human instruction for robots will not be in English, making multilingual VLA capabilities crucial for widespread adoption and equitable access to advanced automation.
This research highlights that while foundational large language models may possess multilingual capacities, their integration into VLA systems for robotics is not automatic and requires dedicated investigation to avoid a 'multilingual gap'.
- · Multilingual AI research teams
- · Global robot manufacturers
- · Developing economies
- · Users of non-English languages
- · English-centric VLA model developers
- · Companies relying on limited language datasets
- · Early adopters with non-English needs
Further research and development will be directed towards creating truly multilingual VLA models.
The global market for advanced robotics will expand as language barriers to entry are reduced.
Ethical considerations around AI bias and accessibility will gain more prominence in VLA development, fostering more inclusive robotic systems.
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