Author

Renhang Liu

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Preprint Sep 2026

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

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 whos...

Ren-Hang Liu, Wen-Zhi Zhao, Zhuo Yang et al. · 7 citations · ⚡4

#artificial intelligence Preprint Oct 2026

RoboQuest: Generalist Physical Agents that Search, Inspect and Test

Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successful operation in an unfamiliar environment may require an agent to seek task-relevant information through interaction when it is absent from the observations: it may need...

Ren-Hang Liu, N. Majumder, Tej Deep Pala et al. · 0 citations

Preprint Sep 2026

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

Genie Envisioner Act 2.0 (GE-Act 2.0) is introduced, a world-action model whose trainable generative and action components are all initialized from scratch on manipulation data, and its components are jointly trained with knowledge-aligned selective optimization (KASO), which reduces mismatched supervision.

AgiBot Research Team, Ren-Hang Liu, Wen-Zhi Zhao et al. · 4 citations · ⚡1