Compositional ManipulationExpert
Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation
This preprint introduces ViGAR, a hierarchical manipulation model that separates visual subgoal planning from subgoal-conditioned world-action generation, using a shared pretrained Cosmos3-Nano world model. On the RoboTwin Clean2Random benchmark it reaches 82.00% Clean and 67.02% Random success, 12.86 points above the best baseline average. It matters because image-based subtask goals allow the same policy to be steered to new task compositions via a global goal image without retraining.