Robotics startup 1X Technologies has developed a new generative model that can make it much more efficient to train robotics systems in simulation. The model, which the company announced in a new blog post, addresses one of the important challenges of robotics, which is learning “world models” that can predict how the world changes in response to a robot’s actions. Given the costs and risks of training robots directly in physical environments, roboticists usually use simulated environments to train their control models before deploying them in the real world. However, the differences between the simulation and the physical environment cause challenges. “Robicists typically hand-author scenes that are a ‘digital twin’ of the real world and use rigid body simulators like Mujoco, Bullet, Isaac to simulate their dynamics,” Eric Jang, VP of AI at 1X Technologies, told VentureBeat. “However, the digital twin may have physics and geometric inaccuracies that lead to training on one environment and deploying on a different one, which causes the ‘sim2real gap.’ For example, the door model you download from the Internet is unlikely to have the same spring stiffness in the handle as the actual door you are testing the robot on.” To bridge this gap, 1X’s new model learns to simulate the real world by being trained on raw sensor data collected directly from the robots. By viewing thousands of hours of video and actuator data collected from the company’s own robots, the model can look at the current observation of the world and predict what will happen if the robot takes certain actions.

Full report : 1X Technologies has developed a new generative model that can make it much more efficient to train robotics systems in simulation.