Correct Me If I am Wrong: Interactive Learning for Robotic Manipulation
Correct Me If I am Wrong: Interactive Learning for Robotic Manipulation
Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Although deep reinforcement learning algorithms have recently demonstrated impressive results in this context, they still require an impractical amount of time-consuming trial-and-error iterations. In this work, we consider the promising alternative paradigm of …