Imitation learning is a popular paradigm to teach robots new tasks, but collecting robot demonstrations through teleoperation or kinesthetic teaching is tedious and time-consuming. In contrast, directly demonstrating a task using our human embodiment is much easier and data is available in abundance, yet transfer to the robot can be non-trivial. In this work, we propose Real2Gen to train a manipulation policy from a single human demonstration. Real2Gen extracts required information from the demonstration and transfers it to a simulation environment, where a programmable expert agent can demonstrate the task arbitrarily many times, generating an unlimited amount of data to train a flow matching policy. We evaluate Real2Gen on human demonstrations from three different real-world tasks and compare it to a recent baseline. Real2Gen shows an average increase in the success rate of 26.6% and better generalization of the trained policy due to the abundance and diversity of training data. We further deploy our purely simulation-trained policy zero-shot in the real world. We make the data, code, and trained models publicly available at real2gen.cs.uni-freiburg.de.

翻译：模仿学习是教授机器人新任务的常用范式，但通过遥操作或示教方式收集机器人演示数据既繁琐又耗时。相比之下，利用人类自身直接演示任务更为简便且数据丰富，然而将其迁移至机器人平台可能面临挑战。本研究提出Real2Gen方法，旨在通过单次人类演示训练机械臂操作策略。Real2Gen从人类演示中提取必要信息并迁移至仿真环境，在该环境中可编程的专家智能体能够无限次演示任务，从而生成海量数据用于训练流匹配策略。我们在三个不同现实任务的人类演示数据上评估Real2Gen，并与近期基线方法进行对比。得益于训练数据的丰富性与多样性，Real2Gen的平均成功率提升26.6%，且训练策略展现出更优的泛化能力。我们进一步将纯仿真训练的部署策略在现实世界中进行了零样本验证。相关数据、代码及训练模型已在real2gen.cs.uni-freiburg.de公开。