IM-ENGINE: Image Editing for Embodied Data Generation

We present Scene2Demo, an automated framework for offline embodied data generation. Given a single RGB scene image and a user query, Scene2Demo constructs an interactive simulated scene and generates executable task configurations, primitive action flows, multi-view execution videos, and offline robot-learning datasets. Its five role-specialized generation modules perform task understanding, symbolic decomposition, BDDL configuration generation, object-size adjustment, and action-flow planning. The resulting object-centric task specifications and action transitions are organized as path search over a scene-conditioned object-action graph. Failed or incomplete executions are refined by two feedback agents: a Robotic Safety Inspector that produces step-wise visual critiques and a Robotic Task Supervisor that revises action sequences or parameters using the rollout and previous evolution history. Across 102 automatically generated primitive scene-task pairs, Scene2Demo achieves a 71.6% execution success rate; on four representative long-horizon tasks, feedback-driven refinement improves both task success and subtask-level execution quality over primitive-only execution. Comparisons with RoboGen and GenSim2 show stronger execution performance under automated data-generation settings. Finally, behavior cloning policies achieve 96.0% and 92.0% success on two representative tasks, showing that the generated data supports downstream policy learning. Our project page is available at https://scene2demo-anon.github.io/.

Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements for multi-view consistency, geometric coherence, and robot embodiment constraints. Existing methods typically adapt foundation models with limited robot data, often sacrificing visual knowledge acquired during large-scale pre-training. We present Xiaomi-Robotics-U0, a 38-billion-parameter multimodal autoregressive model for unified embodied synthesis. It treats embodied generation as an extension of foundation image and video generation and jointly optimizes text-to-image generation, image editing, embodied scene generation, embodied transfer, and embodied video generation. This unified framework preserves the generalization of the pre-trained world foundation model while adapting it to embodied settings. Xiaomi-Robotics-U0 is the first model to support high-quality multi-view scene generation across multiple robot embodiments and to introduce structured, controllable embodied transfer for fine-grained editing while preserving multi-view consistency and interaction dynamics. It achieves state-of-the-art results on single-step and sequential generation tasks, outperforming GPT-Image-2.0 in human evaluations of embodied scene generation and transfer, ranking first on World Arena for embodied video generation, and improving the out-of-distribution success rate of pi_0.5 from 36.9% to 63.2% on challenging real-world manipulation tasks. These results show that foundation world models can serve both as embodied world models and scalable data engines for embodied intelligence. Code and checkpoints are available at https://robotics.xiaomi.com/xiaomi-robotics-u0.html.

Simulation provides a low-cost, scalable pathway to large-scale robotic manipulation data collection. However, existing 3D scene generation methods can rarely be applied directly to manipulation data synthesis, as their generated scenes often lack instance-level interactivity and physical plausibility. Focusing on tabletop manipulation, we propose TabletopGen, a training-free and automated tabletop scene generation and interactive simulation engine. Starting from text or a single image, we first obtain independent 3D object models via generative instance extraction. Second, we introduce a novel pose and scale alignment approach that recovers a collision-free scene layout using a Differentiable Rotation Optimizer and a Top-View Spatial Alignment mechanism. Finally, we assemble the generated scene in a physics simulator with collision geometry, yielding a stable, interactable environment for synthesizing multimodal manipulation data. Extensive experiments and user studies demonstrate that TabletopGen achieves state-of-the-art performance in visual fidelity, layout accuracy, and physical plausibility. Furthermore, we validate the executability of the collected trajectories on a real robotic arm via zero-shot real-to-sim-to-real policy transfer, indicating that TabletopGen can serve as a reliable data engine for robotic manipulation data synthesis.