Robotic deep RL at scale: Sorting waste and recyclables with a fleet of robots – Google AI Blog

Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills, such as robotic grasping, multi-task learning, and even playing table tennis. Although robotic RL has come a long way, we still don't see RL-enabled robots in everyday settings. The real world is complex, diverse, and changes over time, presenting a major challenge for robotic systems. However, we believe that RL should offer us an excellent tool for tackling precisely these challenges: by continually practicing, getting better, and learning on the job, robots should be able to adapt to the world as it changes around them.