G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models

The G$^3$VLA model introduces a camera-aware geometric module for Vision-Language-Action (VLA) systems, enhancing visual-token processing by incorporating calibrated geometric information without modifying the action space. It employs intrinsic-conditioned ray embeddings, projective positional encoding (PRoPE), and bidirectional cross-view fusion, achieving significant performance improvements across various benchmark suites, including LIBERO and RoboCasa24, particularly in spatially sensitive tasks. This development is crucial for practitioners as it addresses the limitations of traditional VLA models in multi-camera environments, enabling more accurate robot manipulation through better alignment of visual information with physical geometry.