The AI developers noted that they created a framework for controlling robots on four legs. She adapts
“Our development teaches a controller that canadapt to changes in the environment along the way. These may also be new scenarios that we did not study during the training. This makes the controller 85% more energy efficient and more reliable than traditional methods, the researchers note. "During inference, the high-level controller only needs to evaluate a small multi-layer neural network, it does not need a control and predictive model (MPC) that would be required to optimize long-term performance."
The model is trained to move using a treadmilltrack, which consists of two belts - their speed changes independently of each other, but the robot still maintains balance. This simulation training is then carried over to the Laikago robot in the real world. Researchers have released a special video about simulations and lab work to popularize the technology.
Experts fromAI domains from Nvidia, the University of California, the University of Texas at Austin, and the University of Toronto. Their design includes a high-level controller using learning amplification and a lower-level controller based on an AI model.
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