SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Models

and real-world setups, where the pre-learned action grids are re-discretized to capture robot-specific spatial action movements of new setups.The superior results from extensive evaluations demonstrate the exceptional in-distribution generalization and out-of-distribution adaptation capability, highlighting the crucial benefit of the proposed spatial-aware representations for generalist robot policy learning.All the details and codes are opensourced.