Robot learning has emerged as a promising tool for taming the complexity and
diversity of the real world. Methods based on high-capacity models, such as
deep networks, hold the promise of providing effective generalization to a wide
range of open-world environments. However, these same methods typically require
large amounts of diverse training data to generalize effectively. In contrast,
most robotic learning experiments are small-scale, single-domain, and
single-robot. This leads to a frequent tension in robotic learning: how can we
learn generalizable robotic controllers without having to collect impractically
large amounts of data for each separate experiment? In this paper, we propose
RoboNet, an open database for sharing robotic experience, which provides an
initial pool of 15 million video frames, from 7 different robot platforms, and
study how it can be used to learn generalizable models for vision-based robotic
manipulation. We combine the dataset with two different learning algorithms:
visual foresight, which uses forward video prediction models, and supervised
inverse models. Our experiments test the learned algorithms' ability to work
across new objects, new tasks, new scenes, new camera viewpoints, new grippers,
or even entirely new robots. In our final experiment, we find that by
pre-training on RoboNet and fine-tuning on data from a held-out Franka or Kuka
robot, we can exceed the performance of a robot-specific training approach that
uses 4x-20x more data. For videos and data, see the project webpage:
this https URL

本文提出 RoboNet，一个用于共享机器人经验的开放数据库，结合视觉先见性和监督逆模型两种学习算法，用于视觉引导的机器人操作的泛化模型学习，其预训练和微调性能超过使用 4 倍至 20 倍更多数据的机器人特定培训方法。