AI researchers challenge a robot to ride a skateboard in simulation

AI researchers say they’ve created a framework for controlling four-legged robots that promises better energy efficiency and adaptability than more traditional model-based gait control of robotic legs. To demonstrate the robust nature of the framework that adjusts to conditions in real time, AI researchers made the system slip on frictionless surfaces to mimic a banana peel, ride a skateboard, and climb on a bridge while walking on a treadmill. An Nvidia spokesperson told VentureBeat that only the frictionless surface test was conducted in real life because of limits placed on office staff size due to COVID-19. The spokesperson said all other challenges took place in simulation. (Simulations are often used as training data for robotics systems before those systems are used in real life.)

“Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is

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