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
BEE is introduced, an intervention-adaptive framework for real-world RL on a frozen VLA that lets the policy go BEyond Expert imitation, and a Correction Model predicts how a human would correct a given VLA proposal and how consistent the correction is along each action dimension.
Wei-Hui Zhao, Xiao Yan, Zu-Nian Wan et al.· 0 citations
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
This work introduces Continual Interactive Distillation for Embodied Reinforcement Learning (CIDER), a continual reinforcement learning framework that freezes the accumulated historical policy as a teacher before learning each new task and interleaves task learning with distillation-based retention.
Hou-Lin Li, Ming Xu, Guofeng Xu et al.· 0 citations
VINE is proposed, an RL-oriented sampling method that enables stable end-to-end value-gradient optimization for flow-matching policies and achieves stable policy improvement and consistently outperforms state-of-the-art RL methods on the OGBench offline RL benchmark and real-world robotic manipulation task.
Rushuai Yang, Zhuo Han, Houlin Li et al.· arXiv.org· 0 citations
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