4D-VLA: Spatiotemporal Vision-Language-Action Pretraining with Cross-Scene Calibration

Understand

Leveraging diverse robotic data for pretraining remains a critical challenge.

• Existing methods typically model the dataset's action distribution using simple observations as inputs.
• However, these inputs are often incomplete, resulting in a dispersed conditional action distribution-an issue we refer to as coordinate system chaos and state chaos.
• This inconsistency significantly hampers pretraining efficiency.