GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
Summary
This paper introduces GE-Act 2.0, a world-action model pretrained from scratch to enable scalable zero-shot robotic manipulation with improved success rates across diverse tasks and conditions.
View Cached Full Text
Cached at: 09/09/26, 04:29 AM
Paper page - GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
Source: https://huggingface.co/papers/2609.05588 Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
GE-Act 2.0 is a world-action model trained from scratch with a control-oriented autoencoder, single-step visual planner, and inverse dynamics model, using knowledge-aligned selective optimization to enable scalable zero-shot robot manipulation across diverse skills and conditions.
World-action models(WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction. Most inherit pretrained video generators, leaving WAM pretraining and scaling underexplored. We introduce Genie Envisioner Act 2.0 (GE-Act 2.0), a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data. It combines acontrol-oriented autoencoder(CoAE), asingle-step visual planner(SVP), and aninverse dynamics model(IDM). CoAE retains action- and instruction-relevant information under aggressive compression, while SVP produces a complete future state in one differentiable pass, so visual planning and inverse dynamics can be pretrained separately on complementary data. The components are then jointly trained withknowledge-aligned selective optimization(KASO), which reduces mismatched supervision by selecting only predicted futures judged behaviorally compatible with the recorded action. We evaluate pretrained checkpoints directly, without per-task fine-tuning, on 100 tasks across 20 manipulation skill groups with held-out scenes, backgrounds, lighting, and object instances. Scaling co-training data from 300 to 30,000 hours raises success from 17.1% to 44.1% on G1-OP and from 13.4% to 31.1% on G2-90D; despite comprising less than 2% of the co-training data, G2-90D improves by 17.7 points, suggestingcross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage strongly correlates withzero-shot out-of-distribution(OOD) success (Pearson r=0.80; Spearman rho=0.85). Under the same protocol, the model grounds object, color, shape, and position references in at least 90% of trials and follows explicit instructions even when they conflict with an already-committed behavior or a conventional scene association.
View arXiv pageView PDFProject pageAdd to collection
Get this paper in your agent:
hf papers read 2609\.05588
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2609.05588 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2609.05588 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2609.05588 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
ZimaBlue introduces a scalable framework for learning generalizable world action models from large-scale egocentric video, substantially improving zero-shot robotic manipulation through a three-stage curriculum and slow-fast architecture.
Geometric Action Model for Robot Policy Learning
The Geometric Action Model (GAM) repurposes a pretrained geometric foundation model (GFM) as a unified backbone for language-conditioned robot manipulation, achieving higher accuracy, robustness, and efficiency than existing foundation-model-scale baselines across simulation and real-world benchmarks.
Dyna-2: A 1-Million-Hour Scaling Law for World-Action Models (29 minute read)
Dyna-2 is a world-action model pre-trained on over a million hours of human video, showing scaling laws on human data and a human-to-robot transfer scaling law for zero-shot robot performance.
ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
ABot-M0.5 is a new World Action Model for mobile manipulation that improves performance through temporal granularity alignment, action space disentanglement, and train-test consistency, achieving state-of-the-art results on long-horizon and fine-grained manipulation benchmarks.
Learning Transferable Dynamics Priors from Action to World Modeling
This paper introduces A2World, a diffusion-based world model pretrained on large-scale robot manipulation data to learn transferable dynamics priors. The model can be adapted into a real-world simulator (A2World-sim) for policy evaluation or a video-action prediction model (A2World-policy) for action prediction, demonstrating benefits for both simulator-centric and policy-centric robot learning.