Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

• URL: http://arxiv.org/abs/2508.05635v3
• Date: Tue, 04 Nov 2025 12:01:01 GMT
• Title: Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
• Authors: Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, Liliang Chen, Shuicheng Yan, Maoqing Yao, Guanghui Ren,
• Abstract summary: We introduce Genie Envisioner (GE), a unified world foundation platform for robotic manipulation.<n>GE integrates policy learning, evaluation, and simulation within a single video-generative framework.
• Score: 69.30586607892842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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