Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies

Abstract

While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models trained in simulation to a real-world robotic task. We introduce a bottleneck between perception and control, enabling the networks to be trained independently, but then merged and fine-tuned in an end-to-end manner to further improve hand-eye coordination. On a canonical, planar visually-guided robot reaching task a fine-tuned accuracy of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5 pixels), showing the potential for more complicated and broader applications. Our method provides a technique for more efficient learning and transfer of visuo-motor policies for real robotic systems without relying entirely on large real-world robot datasets.

Australasian Conference on Robotics and Automation (ACRA) 2017, Student Paper Award Finalist

Cited by in corpus (4)

• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
• Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
• Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
• Sim-to-Real Transfer of Robot Learning with Variable Length Inputs