The human hand is one of the fascinating creations of nature, and one of the highly sought goals of artificial intelligence and robotics researchers. A robotic hand that could manipulate objects as we do would be enormously useful in factories, warehouses, offices, and homes.
Yet despite tremendous progress in the field, research on robotics hands remains extremely expensive and limited to a few very wealthy companies and research labs.
Now, new research promises to make robotics research available to resource-constrained organizations. In a paper published on arXiv, researchers at the University of Toronto, Nvidia, and other organizations have presented a new system that leverages highly efficient deep reinforcement learning techniques and optimized simulated environments to train robotic hands at a fraction of the costs it would normally take.
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