Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
This addresses the need for a practical and scalable foundation for general-purpose robotic manipulation, though it appears incremental as it builds upon existing video diffusion and simulation methods.
The authors tackled the problem of robotic manipulation by introducing Genie Envisioner, a unified platform that integrates policy learning, evaluation, and simulation using a video-generative framework, resulting in a scalable system for instruction-driven embodied intelligence with components like GE-Base, GE-Act, and GE-Sim.
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.