发表机构
Robotics and AI Institute; Imperial College London（机器人与人工智能研究所; 伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Simify框架，利用生成式真实到仿真重建与大规模并行物理仿真，在测试时进行显式空间推理，通过进化搜索优化物体排列，在真实机器人上实现端到端复杂重排任务，优于先前基础模型方法。
AI 中文摘要
空间推理是通用机器人智能的基础，它使机器人能够完成涉及多物体交互的长时程任务。我们提出了Simify，一个无需训练、测试时的框架，通过大规模并行物理仿真进行显式空间推理。从场景的单张RGB-D图像出发，Simify利用3D生成模型和视觉语言模型重建可直接用于仿真的资产。随后，给定由奖励函数指定的任务（例如，搭建最高的塔），Simify在仿真中启动数千次并行滚动，并执行进化搜索以优化物体排列，通常在数秒内收敛。我们在真实机器人硬件上进行了定量实验，以展示我们的框架对未见过的物体端到端执行复杂物体重排任务的能力。结果表明，我们的框架通过在推理期间有效利用大规模并行仿真，在空间推理的基础模型上优于先前工作，并强调了完整且准确的几何对于成功的仿真到现实迁移的重要性。
英文摘要
Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction. We introduce Simify, a training-free, test-time framework that performs explicit spatial reasoning via massively parallel physics simulation. From a single RGB-D image of a scene, Simify reconstructs simulation-ready assets leveraging 3D generative models and vision-language models. Then given a task specified by a reward function (e.g., build the tallest tower), Simify launches thousands of parallel rollouts in simulation and performs an evolutionary search to optimize object arrangements, typically converging within seconds. We conduct quantitative experiments on real-robot hardware to demonstrate the ability of our framework to execute complex object rearrangement tasks end-to-end with previously unseen objects. Results show that our framework outperforms prior work on foundation models for spatial reasoning by effectively exploiting large-scale parallel simulation during inference, and also highlight the importance of complete and accurate geometry for successful sim-to-real transfer.
CommentsIROS 2026
