ResCue: Residual Spatial Cueing for Language-Conditioned Imitation Learning
ResCue decouples semantic reasoning from spatial control in Vision-Language-Action (VLA) models to improve imitation learning under data constraints. It translates language instructions into explicit Spatial Visual Prompts (SVP) using a vision model like SAM 3. These prompts are fused directly into the action generator, providing uncorrupted spatial guidance that significantly boosts performance on complex tasks with minimal demonstrations.