Generalizing from Simulation

Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unplanned changes in the environment as they solve simple tasks. That is, we've used these techniques to build closed-loop systems rather than open-loop ones as before. The simulator need not match the real-world in appearance or dynamics; instead, we randomize relevant aspects of the environment, from friction to action delays to sensor noise. Our new results provide more evidence that general-purpose robots can be built by training entirely in simulation, followed by a small amount of self-calibration in the real world. This robot was trained in simulation with dynamics randomization to push a puck to a goal.