{"ID":2885386,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05635","arxiv_id":"2508.05635","title":"Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation","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.","short_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, tempora...","url_abs":"https://arxiv.org/abs/2508.05635","url_pdf":"https://arxiv.org/pdf/2508.05635v3","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\"]","published":"2025-08-07T17:59:44Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}