机器人看世界：从2D视频到4D物理模型
现在的机器人看世界就像看2D电影——它看到的是像素，不是物理。这篇论文让机器人同时理解物体的外观、3D形状和运动轨迹，就像人眼一样。研究者训练了一个模型，输入一张带深度的照片和一句指令（比如“把杯子拿起来”），就能预测接下来几秒的RGB画面、深度图和运动流，三者同步生成。更关键的是，他们用这个4D模型直接输出机器人动作，绕过了传统方法中“先预测画面再规划动作”的繁琐步骤。在真实双臂机器人任务上，这套系统在需要空间精度和时间配合的操作（比如双手拧瓶盖）上达到了当前最好水平。它不是你明天就能用的工具，但指明了方向：让机器人从“看视频”进化到“理解物理世界”。
📄 原文摘要(英文)
Robotic manipulation in the open world requires not only recognizing what a scene looks like, but also anticipating how its 3D structure moves under interaction. We argue that synchronized RGB, depth, and optical flow, namely RGB-DF, provide a physically grounded representation that captures the underlying 4D dynamics of a scene. Compared to 2D pixel videos, this multi-modal synergy aligns visual appearance with geometric structure and temporal motion, creating a representation space significantly closer to the low-level end-effector actions demanded by robotic systems, thereby narrowing the gap between world prediction and policy learning. Building on this insight, we introduce RynnWorld-4D, a generative model that co-produces future RGB frames, depth maps, and optical flow from a single RGB-D image and a language instruction within one unified diffusion process. This 4D world model features a tri-branch architecture that integrates cross-modal attention with frame-wise 3D RoPE, ensuring that appearance, geometry, and motion evolve consistently. To supply training data at scale, we curate Rynn4DDataset 1.0, a massive dataset of over 254.4 million frames across egocentric human and robotic manipulation videos with high-quality pseudo-labels for depth and optical flow. We further propose RynnWorld-4D-Policy, an inverse dynamics head that consumes the internal 4D representations of RynnWorld-4D in a single forward pass, bypassing expensive multi-step denoising, to output robot actions in a closed-loop manner. Experiments show that RynnWorld-4D produces temporally and spatially coherent 4D predictions, and that RynnWorld-4D-Policy achieves state-of-the-art performance on real-world dexterous bimanual manipulation tasks, particularly excelling in tasks demanding spatial precision and temporal coordination.