Learning One-Shot Imitation From Humans Without Humans
Learning One-Shot Imitation From Humans Without Humans
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DOI：
10.1109/lra.2020.2977835
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发表时间：
2020-04-01
影响因子：
5.2
通讯作者：
Davison, Andrew J.
中科院分区：
文献类型：
--
作者：
Bonardi, Alessandro;James, Stephen;Davison, Andrew J.
人类可以通过观察其他个体执行一次新任务来自然地学习执行该任务，然后以各种配置复制它。赋予机器人这种从第三人称模仿人类的能力是教授新任务的一种非常直接和自然的方式。直到最近，通过元学习，已经有成功的尝试一次性模仿人类学习;然而，这些方法需要大量的人力资源来收集真实的世界中的数据来训练机器人。但有没有一种方法可以消除训练过程中对真实的世界人类演示的需求？我们表明，与任务嵌入式控制网络，我们可以推断控制策略，通过嵌入人的演示，可以条件的控制策略，实现一次性模仿学习。重要的是，我们没有使用真实的人类手臂在训练过程中提供演示，而是在一个以前从未见过的应用中利用域随机化：人类的模拟到真实的转移。在评估我们在模拟和真实的世界中推送和放置任务的方法时，我们表明，与在真实世界数据上训练的系统相比，我们能够通过仅利用模拟数据来实现类似的结果。视频可以在这里找到：https://sites.google.com/view/tecnets-humans。
Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data. Videos can be found here: https://sites.google.com/view/tecnets-humans.