> For the complete documentation index, see [llms.txt](https://docs.overthereality.ai/over-wiki/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.overthereality.ai/over-wiki/~/revisions/uHMWl1Q4UR2oZMyfWgfL/physical-ai/physical-ai-foundation-models-for-robotics/vision-language-action-vla-models.md).

# Vision-Language-Action (VLA) Models

**Vision-Language-Action (VLA)** models bridge the gap between human instruction and robotic action. These models are trained to connect natural language commands with visual input from the robot's camera and translate that understanding into executable motor commands. Instead of needing complex code, a user can simply tell the robot what to do.

For example, a user could say, "Please pick up the blue cup next to the laptop." The VLA model would:

1. **Vision:** Process the camera feed to locate the "blue cup" and the "laptop."
2. **Language:** Understand the intent behind the command "pick up" and the spatial relationship "next to."
3. **Action:** Generate the precise sequence of motor commands (e.g., move arm to coordinates, open gripper, lower arm, close gripper, lift arm) to execute the task.

VLAs make human-robot interaction intuitive and accessible, paving the way for robots in more collaborative and dynamic settings.

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