#G1-OP (1)

GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

GE-Act 2.0 combines a control-oriented autoencoder, a single-step visual planner, an inverse dynamics model, and knowledge-aligned selective optimization to pretrain a world-action policy from scratch on manipulation data. Scaling co-training data from 300 to 30,000 hours raises zero-shot OOD success to 44.1% on G1-OP and 31.1% on G2-90D without task-specific fine-tuning.