一个统一平台让机器人学会按指令操作，生成真实动作并自动评估。
Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
- 用视频扩散模型学习真实机器人的动作规律，建模空间时间语义关系。
- 轻量解码器将抽象表示转为可执行动作，少标注也能跨机型通用。
- 自带仿真与评测工具，适合做通用机器人智能研究的开发者使用。
我们提出Genie Envisioner（GE），一个面向机器人操作的统一世界基础平台，将策略学习、评估与仿真集成于单一视频生成框架中。核心GE-Base是一个大规模、指令条件化的视频扩散模型，以结构化潜在空间捕捉真实世界机器人交互中的空间、时间与语义动态。基于此，GE-Act通过轻量级流匹配解码器将潜在表示映射为可执行动作轨迹，实现低监督下多种机器人形态的精准且可泛化的策略推理。为支持可扩展的训练与评估，GE-Sim作为动作条件化的神经仿真器，生成高保真回放数据以支持闭环策略开发。平台还配备EWMBench基准套件，用于衡量视觉保真度、物理一致性及指令-动作对齐程度。上述组件共同构建了一个可扩展、实用的指令驱动型通用具身智能基础平台。所有代码、模型与基准将公开发布。
原文摘要 · Abstract (English)
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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