BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
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发表时间：
2022-02
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通讯作者：
Eric Jang;A. Irpan;Mohi Khansari;Daniel Kappler;F. Ebert;Corey Lynch;S. Levine;Chelsea Finn
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作者：
Eric Jang;A. Irpan;Mohi Khansari;Daniel Kappler;F. Ebert;Corey Lynch;S. Levine;Chelsea Finn
在本文中，我们研究的问题，使基于视觉的机器人操作系统推广到新的任务，在机器人学习的一个长期的挑战。我们从模仿学习的角度来处理这一挑战，旨在研究如何扩展和扩大收集的数据可以促进这种泛化。为此，我们开发了一个交互式和灵活的模仿学习系统，可以从演示和干预中学习，并且可以根据传达任务的不同形式的信息进行调整，包括预先训练的自然语言嵌入或人类执行任务的视频。当将真实的机器人上的数据收集扩展到100多个不同的任务时，我们发现该系统可以执行24个看不见的操作任务，平均成功率为44%，而无需对这些任务进行任何机器人演示。
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach the challenge from an imitation learning perspective, aiming to study how scaling and broadening the data collected can facilitate such generalization. To that end, we develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions and can be conditioned on different forms of information that convey the task, including pre-trained embeddings of natural language or videos of humans performing the task. When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%, without any robot demonstrations for those tasks.