Reinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot's arm.

该研究采用增强学习的方法，通过深度空间自编码器从摄像头图像中直接学习状态表征，以实现对环境进行特征点提取和控制的闭环控制，展示了PR2机器人在推动自由玩具方块、使用铲子拿起一袋米和在不同位置将绳环悬挂在钩子上等任务中自动学习控制的成功案例。

深度空间自编码器用于视觉机器人学习