Operating directly from raw high dimensional sensory inputs like images is
still a challenge for robotic control. Recently, Reinforcement Learning methods
have been proposed to solve specific tasks end-to-end, from pixels to torques.
However, these approaches assume the access to a specified reward which may
require specialized instrumentation of the environment. Furthermore, the
obtained policy and representations tend to be task specific and may not
transfer well. In this work we investigate completely self-supervised learning
of a general image embedding and control primitives, based on finding the
shortest time to reach any state. We also introduce a new structure for the
state-action value function that builds a connection between model-free and
model-based methods, and improves the performance of the learning algorithm. We
experimentally demonstrate these findings in three simulated robotic tasks.

本研究探讨了完全自我监督的学习方法，基于状态达成最短时间来实现通用图像嵌入和控制基元，同时介绍了一种新的状态操作价值函数结构，建立了模型自由和模型基础方法之间的联系，并提高了学习算法的性能。三项模拟机器人任务的实验结果表明了这些发现。