End-to-End Training of Deep Visuomotor Policies

TL;DR

This paper demonstrates that end-to-end training of deep CNN-based visuomotor policies, combining perception and control, improves robotic manipulation performance over separate training methods.

Contribution

The authors introduce a method for jointly training perception and control policies end-to-end using deep CNNs and guided policy search, enabling direct mapping from raw images to motor torques.

Findings

End-to-end trained policies outperform separately trained components.

The method successfully handles real-world manipulation tasks.

Joint training improves coordination between vision and control.

Abstract

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.