Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, however, existing data sources force an unsatisfying tradeoff between trajectory quality and scalability. Real-world teleoperation provides the highest-quality trajectories but requires dedicated physical space and time-consuming scene resets. Simulation offers an alternative way out of this dilemma: it can produce clean, embodiment-aligned data at scale without any physical hardware. In this paper, we propose OASIS, a simulation-data-driven framework for humanoid loco-manipulation. OASIS automatically reconstructs realistic object assets from real-world images using a 3D generative model. Based on these assets, trajectories are first collected through teleoperation in simulation, and then augmented under diverse domain randomizations in a post-processing stage. With the resulting simulation data, we further design a hierarchical visuomotor policy for humanoid loco-manipulation. Extensive experiments on the real humanoid robot show that, under zero-shot deployment, the policy trained on our simulation data achieves higher success rates on most tasks than that trained on real-robot teleoperation data, owing largely to the broad lighting and environmental variations covered by our simulation rendering, which real-robot data fails to capture. The project page is available at https://oasis-humanoid.github.io/.

翻译：近期机器人操作领域的进展很大程度上得益于从大规模示教数据中学习。然而，对于人形机器人移动操作任务而言，现有数据源在轨迹质量与可扩展性之间存在难以令人满意的权衡。真实世界遥操作可提供最高质量的轨迹，但需要专用物理空间和耗时的场景重置。仿真为此困境提供了替代途径：无需任何物理硬件即可大规模生成干净、与本体形态一致的数据。本文提出OASIS——一种面向人形机器人移动操作的仿真数据驱动框架。OASIS利用3D生成模型从真实世界图像自动重建逼真的物体资产。基于这些资产，首先通过仿真中的遥操作收集轨迹，随后在后处理阶段通过多样化域随机化进行数据增强。利用所生成的仿真数据，我们进一步设计了用于人形机器人移动操作的分层级视觉运动策略。在真实人形机器人上的大量实验表明，在零样本部署条件下，基于仿真数据训练的策略在大多数任务上的成功率均高于基于真实机器人遥操作数据训练的策略，这主要归因于我们的仿真渲染涵盖了广阔的光照与环境变化，而真实机器人数据无法捕获这些变化。项目页面详见 https://oasis-humanoid.github.io/。