supersizing self-supervision: learning to grasp from 50k tries and 700 robot hours
Author(s): Lerrel Pinto, Abhinav Gupta
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2015
Keywords: manipulation, reinforcement learning, neural networks
Expert Opinion: This paper demonstrated that it's possible to have a robot interact in a self-supervised way with the environment in order to learn useful tasks, like grasping. By running a robot for a long period of time, it's possible to collect enough data to train policies using simple algorithms. This lead the way for a lot of follow up work from Google and others, and is likely an area where we'll see a lot of interest in the future.