通过生成基础模型扩展单人示范用于模仿学习
Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models
AI总结：
通过生成基础模型扩展单人示范用于模仿学习，实现从单人示范到机器人任务的高效训练与泛化
摘要：
模仿学习是一种流行的方法，用于教授机器人新任务，但通过远程操作或力反馈教学收集机器人示范是繁琐且耗时的。相比之下，直接使用我们的身体示范进行任务演示要容易得多，数据丰富，但转移到机器人可能并不容易。在本工作中，我们提出了Real2Gen，用于从单个人的示范中训练操控策略。Real2Gen从示范中提取所需信息，并将其转移到模拟环境，在其中可编程专家代理可以任意多次演示任务，生成无限量的数据以训练流匹配策略。我们在三个不同的现实任务中评估了Real2Gen，并将其与最近的一个基线进行比较。Real2Gen在成功率上平均提高了26.6%，并且由于训练数据的丰富性和多样性，训练的策略具有更好的泛化能力。我们进一步在现实世界中部署了我们纯模拟训练的策略进行零样本测试。我们公开了数据、代码和训练好的模型，网址为real2gen.cs.uni-freiburg.de。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
