Prediction is an appealing objective for self-supervised learning of
behavioral skills, particularly for autonomous robots. However, effectively
utilizing predictive models for control, especially with raw image inputs,
poses a number of major challenges. How should the predictions be used? What
happens when they are inaccurate? In this paper, we tackle these questions by
proposing a method for learning robotic skills from raw image observations,
using only autonomously collected experience. We show that even an imperfect
model can complete complex tasks if it can continuously retry, but this
requires the model to not lose track of the objective (e.g., the object of
interest). To enable a robot to continuously retry a task, we devise a
self-supervised algorithm for learning image registration, which can keep track
of objects of interest for the duration of the trial. We demonstrate that this
idea can be combined with a video-prediction based controller to enable complex
behaviors to be learned from scratch using only raw visual inputs, including
grasping, repositioning objects, and non-prehensile manipulation. Our
real-world experiments demonstrate that a model trained with 160 robot hours of
autonomously collected, unlabeled data is able to successfully perform complex
manipulation tasks with a wide range of objects not seen during training.

本文提出了一种方法，从原始图像观测中自动学习机器人技能，利用自主收集的经验，通过结合自监督算法对图像对准的学习和基于视频预测的控制器进行组合，能够使机器人从零开始，仅使用原始视觉输入就可以学习到复杂的行为，并进行成功的复杂操作，该方法在实验中表现出了很好的结果。