HybridVLA integrates two complementary AI approaches to enable more precise robotic manipulation by leveraging language understanding and vision-guided actions.

• Solves the action continuity problem that plagues traditional autoregressive models
• Creates a unified architecture that collaboratively combines diffusion and autoregression
• Demonstrates superior performance in both simulated and real-world robotic systems
• Enhances robots' ability to follow natural language instructions for complex manipulation tasks

This research advances engineering capabilities by bridging the gap between AI's language understanding and physical robot control, creating more versatile automation systems for manufacturing environments.

HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model