Training robot policies in the real world can be unsafe, costly, and difficult to scale. Simulation serves as an inexpensive and potentially limitless source of training data, but suffers from the semantics and physics disparity beween simulated and real-world environments. These discrepancies can be minimized by training in digital twins,which serve as virtual replicas of a real scene but are expensive to generate and cannot produce cross-domain generalization. To address these limitations, we propose the concept of digital cousins, a virtual asset or scene that, unlike a digital twin,does not explicitly model a real-world counterpart but still exhibits similar geometric and semantic affordances. As a result, digital cousins simultaneously reduce the cost of generating an analogous virtual environment while also facilitating better robustness during sim-to-real domain transfer by providing a distribution of similar training scenes. Leveraging digital cousins, we introduce a novel method for the Automatic Creation of Digital Cousins (ACDC), and propose a fully automated real-to-sim-to-real pipeline for generating fully interactive scenes and training robot policies that can be deployed zero-shot in the original scene. We find that ACDC can produce digital cousin scenes that preserve geometric and semantic affordances, and can be used to train policies that outperform policies trained on digital twins, achieving 90% vs. 25% under zero-shot sim-to-real transfer. Additional details are available at https://digital-cousins.github.io/.

翻译：在现实世界中训练机器人策略可能不安全、成本高昂且难以扩展。仿真作为一种廉价且可能无限的训练数据来源，却受限于模拟环境与现实环境之间的语义与物理差异。通过在数字孪生中进行训练可以最小化这些差异——数字孪生作为真实场景的虚拟复现，但其生成成本高昂且无法实现跨领域泛化。为应对这些局限，我们提出数字表亲的概念：与数字孪生不同，这种虚拟资产或场景并不显式建模现实世界的对应物，但仍呈现相似的几何与语义可供性。因此，数字表亲在降低生成类比虚拟环境成本的同时，通过提供相似训练场景的分布，促进了仿真到现实领域迁移过程中更好的鲁棒性。基于数字表亲，我们提出一种自动创建数字表亲（ACDC）的新方法，并构建了从真实到仿真再到真实的全自动流程，用于生成完全可交互的场景并训练机器人策略，该策略可在原始场景中实现零样本部署。实验表明，ACDC生成的数字表亲场景能保持几何与语义可供性，且基于其训练的策略性能优于数字孪生训练的策略，在零样本仿真到现实迁移中分别达到90%与25%的成功率。更多细节详见 https://digital-cousins.github.io/。