HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model
HARP addresses inconsistencies in Vision-Language-Action (VLA) pretraining from human videos by using paired human-robot demonstrations and unpaired data to align visual representations and latent actions. It introduces a framework that trains robot encoders and latent action models with manipulation cues, resulting in improved robot policy performance.