Genie Envisioner: A Unified World Foundation Platform for Robotic
  Manipulation
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.
689554ee48b0ae5ca2710ce7
Yue Liao
689554ee48b0ae5ca2710ce8
Pengfei Zhou
689554ee48b0ae5ca2710ce9
Siyuan Huang
claimed_verified
2025-08-11T06:49:52.302Z
634e4120038b5879133552f5
/avatars/34ec861b4bbf1aecf927a7d6e726c7a4.svg
Siyuan
user
SiyuanH
689554ee48b0ae5ca2710cea
Donglin Yang
689554ee48b0ae5ca2710ceb
Shengcong Chen
689554ee48b0ae5ca2710cec
Yuxin Jiang
689554ee48b0ae5ca2710ced
Yue Hu
689554ee48b0ae5ca2710cee
Jingbin Cai
689554ee48b0ae5ca2710cef
Si Liu
689554ee48b0ae5ca2710cf0
Jianlan Luo
claimed_verified
2025-08-08T16:20:22.677Z
64f8cb8ed04a890f5380d9a4
/avatars/d6fdfdbb0c10141aa3b4c832d928121b.svg
Jianlan Luo
user
jianlanluo
689554ee48b0ae5ca2710cf1
Liliang Chen
689554ee48b0ae5ca2710cf2
Shuicheng Yan
689554ee48b0ae5ca2710cf3
Maoqing Yao
689554ee48b0ae5ca2710cf4
Guanghui Ren
claimed_verified
2025-08-08T16:20:27.011Z
646ec9b135f55eb49e405faa
/avatars/a17194be585d20e2a021e77a5a20e213.svg
Guanghui Ren
user
sundrops
Genie Envisioner: A Unified World Foundation Platform for Robotic
  Manipulation
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.
Genie Envisioner integrates policy learning, evaluation, and simulation using a video diffusion model and neural simulator for instruction-driven robotic manipulation.
video diffusion model latent space flow-matching decoder action trajectories neural simulator high-fidelity rollouts EWMBench visual fidelity physical consistency instruction-action alignment