The ability to reuse collected data and transfer trained policies between robots could alleviate the burden of additional data collection and training. While existing approaches such as pretraining plus finetuning and co-training show promise, they do not generalize to robots unseen in training. Focusing on common robot arms with similar workspaces and 2-jaw grippers, we investigate the feasibility of zero-shot transfer. Through simulation studies on 8 manipulation tasks, we find that state-based Cartesian control policies can successfully zero-shot transfer to a target robot after accounting for forward dynamics. To address robot visual disparities for vision-based policies, we introduce Mirage, which uses "cross-painting"--masking out the unseen target robot and inpainting the seen source robot--during execution in real time so that it appears to the policy as if the trained source robot were performing the task. Mirage applies to both first-person and third-person camera views and policies that take in both states and images as inputs or only images as inputs. Despite its simplicity, our extensive simulation and physical experiments provide strong evidence that Mirage can successfully zero-shot transfer between different robot arms and grippers with only minimal performance degradation on a variety of manipulation tasks such as picking, stacking, and assembly, significantly outperforming a generalist policy. Project website: https://robot-mirage.github.io/

翻译：重用收集的数据并在机器人之间迁移训练好的策略，能够减轻额外数据收集和训练的负担。尽管现有方法如预训练加微调和协同训练显示出潜力，但它们无法泛化到训练中未见过的机器人。聚焦于具有相似工作空间和二指夹爪的常见机械臂，我们研究了零样本迁移的可行性。通过对8项操作任务的仿真研究，我们发现基于状态的笛卡尔控制策略在考虑前向动力学后，能够成功零样本迁移到目标机器人。为了解决基于视觉的策略中机器人视觉差异的问题，我们提出了幻影（Mirage），它在实时执行过程中使用“跨绘制”——遮挡未见过的目标机器人并绘制已见过的源机器人——使得策略看起来像是训练过的源机器人在执行任务。幻影适用于第一人称和第三人称相机视角，以及接受状态和图像作为输入或仅接受图像作为输入的策略。尽管方法简单，我们广泛的仿真和物理实验提供了强有力的证据，表明幻影能够在不同的机械臂和夹爪之间成功实现零样本迁移，在拾取、堆叠和组装等多种操作任务上仅出现轻微的性能下降，显著优于通用策略。项目网站：https://robot-mirage.github.io/