Offline Meta-Reinforcement Learning for Industrial Insertion
Tony Z. Zhao,Jianlan Luo,Oleg Sushkov,Rugile Pevceviciute,Nicolas Heess,Jon Scholz,Stefan Schaal,Sergey Levine,Tony Z. Zhao,Jianlan Luo,Oleg Sushkov,Rugile Pevceviciute,Nicolas Heess,Jon Scholz,Stefan Schaal,Sergey Levine
Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper, we tackle rapid adaptation to new tasks through the framework of meta-learning, which utilizes past tasks to learn to adapt with a specific focus on industrial insertion tasks. Fast adaptation is crucial because prohibi...
