This research transfers powerful vision-language-action (VLA) models from fixed-base robots to mobile manipulation robots, enabling them to perform complex tasks across varied environments.

• Introduces a novel framework that combines VLA models with mobile navigation capabilities
• Achieves generalization across tasks and environments without requiring large-scale training
• Implements a unified planning approach that coordinates robot movement and manipulation
• Demonstrates practical applications for assistive robotics in everyday settings

This breakthrough addresses a fundamental engineering challenge in robotics: creating mobile manipulation systems that can understand natural language instructions and adapt to diverse real-world scenarios, bringing us closer to versatile robotic assistants.

MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation