“Our work shows that training robots in the real world is more feasible than previously thought, and we hope, as a result, to empower other researchers to start tackling more real-world problems,” said Laura Smith, a Ph.D. student in Levine’s lab and one of the lead authors of the paper posted on arXiv.In past studies, robots of comparable complexity required several hours to weeks of data input to learn to walk using reinforcement learning (RL).The researchers then trained the robot within this world model, with the robot using it to imagine potential outcomes, a process they call “training in imagination.” In a paper posted on arXiv, Abbeel and his team looked at how the Dreamer algorithm and world model could enable faster learning on physical robots in the real world, without simulators or demonstrations.“The robot sort of dreams and imagines what the consequences of its actions would be and then trains itself in imagination to improve, thinking of different actions and exploring a different sequence of events,” said Escontrela.Even if the robot takes a bad fall in the process of exploring or is pushed down, and recovery seems impossible, it still must try to recover on its own without any intervention."