Genie Envisioner: A Unified World Foundation Platform for Robotic
  Manipulation
Authors: name: Yue Liao, name: Pengfei Zhou, name: Siyuan Huang, name: Donglin Yang, name: Shengcong Chen, name: Yuxin Jiang, name: Yue Hu, name: Jingbin Cai, name: Si Liu, name: Jianlan Luo, name: Liliang Chen, name: Shuicheng Yan, name: Maoqing Yao, name: Guanghui Ren

We introduce Genie Envisioner (GE), a unified world foundation platform for
robotic manipulation that integrates policy learning, evaluation, and
simulation within a single video-generative framework. At its core, GE-Base is
a large-scale, instruction-conditioned video diffusion model that captures the
spatial, temporal, and semantic dynamics of real-world robotic interactions in
a structured latent space. Built upon this foundation, GE-Act maps latent
representations to executable action trajectories through a lightweight,
flow-matching decoder, enabling precise and generalizable policy inference
across diverse embodiments with minimal supervision. To support scalable
evaluation and training, GE-Sim serves as an action-conditioned neural
simulator, producing high-fidelity rollouts for closed-loop policy development.
The platform is further equipped with EWMBench, a standardized benchmark suite
measuring visual fidelity, physical consistency, and instruction-action
alignment. Together, these components establish Genie Envisioner as a scalable
and practical foundation for instruction-driven, general-purpose embodied
intelligence. All code, models, and benchmarks will be released publicly.