🤖 Turns out robot dexterity scales with data too, and now there's hard evidence for it. Title: GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation URL: 🧩 Overview AgiBot Research Team built GE-Act 2.0, a world-action model that predicts future visual states to guide robot actions, training every component from scratch on manipulation data instead of inheriting a pretrained video generator. ⚙️ Problem it solves Prior systems reused reconstruction-optimized video generators that don't guarantee action-relevant information, and it was unclear how to pretrain visual generation and inverse dynamics separately before connecting them. 🛠 Methodology Three pieces make it work: a 64x-compressed control-oriented autoencoder (CoAE), a Single-Step Visual Planner that generates a full future frame in one forward pass, and KASO, which fixes the "validity gap" between predicted futures and real actions via top-k selection. 📊 Results Scaling training data from 300 to 30,000 hours lifted the G1-OP robot's success rate from 17.1% to 44.1%, a 27.0-point gain. Even G2-90D, with under 2% of the data, gained 17.7 points, showing real cross-embodiment transfer. Object, color, and position grounding all exceed 90% accuracy. #Robotics# #WorldModels#