Policy search methods based on reinforcement learning and optimal control can allow robots to automatically learn a wide range of tasks. However, practical applications of policy search tend to require the policy to be supported by hand-engineered components for perception, state estimation, and low-level control. We propose a method for learning policies that map raw, low-level observations, consisting of joint angles and camera images, directly to the torques at the robot's joints. The policies are represented as deep convolutional neural networks (CNNs) with 92,000 parameters. The high dimensionality of such policies poses a tremendous challenge for policy search. To address this challenge, we develop a sensorimotor guided policy search method that can handle high-dimensional policies and partially observed tasks. We use BADMM to decompose policy search into an optimal control phase and supervised learning phase, allowing CNN policies to be trained with standard supervised learning techniques. This method can learn a number of manipulation tasks that require close coordination between vision and control, including inserting a block into a shape sorting cube, screwing on a bottle cap, fitting the claw of a toy hammer under a nail with various grasps, and placing a coat hanger on a clothes rack.

该研究旨在回答：联合端到端训练感知和控制系统是否比单独训练每个组件提供更好的性能？通过使用卷积神经网络表示，并在一系列需要视觉和控制之间紧密协作的真实世界操纵任务上进行评估。

深度视觉动作策略的端到端训练