GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
cs.RO, cs.CV
Submitted: 2026-09-04
Updated: 2026-10-09
Code: https://github.com/NVIDIA/Isaac-GR00T
Project page: https://ge-act-v2.github.io/Abstract
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-1: Robotics Transformer for Real-World Control at Scale
- Genie: Generative Interactive Environments
- GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
- Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
- ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
- Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videos
- Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- One Step Diffusion via Shortcut Models
- Mean Flows for One-step Generative Modeling
- Improved Mean Flows: On the Challenges of Fastforward Generative Models
- Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation
- Ctrl-World: A Controllable Generative World Model for Robot Manipulation
- LTX-Video: Realtime Video Latent Diffusion
- Learning Latent Dynamics for Planning from Pixels
- Dream to Control: Learning Behaviors by Latent Imagination
- Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
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