Deep Reinforcement Learning for Vision-Based Robotic Grasping
A Simulated Comparative Evaluation of Off-Policy Methods
Deirdre Quillen*, Eric Jang*, Ofir Nachum*, Chelsea Finn, Julian Ibarz, and Sergey Levine
Google Brain Robotics and Berkeley EECS
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training.
We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms.
This paper will be presented at the International Conference on Robotics and Automation (ICRA), May 2018.