This is the latest achievement published in the Science subjournal, Science Robotics, by research institutions including ...

The core of this research lies in the combination of Graph Neural Networks (GNN) and Reinforcement Learning to achieve coordinated control of up to eight robotic arms, enabling efficient and collision ...

A U.S. Naval Research Laboratory (NRL) research team successfully conducted the first reinforcement learning (RL) control of ...

Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques.

Google DeepMind and Intrinsic developed AI that uses graph neural networks and reinforcement learning to automate multi-robot ...

Deep learning can be applied to different learning paradigms, LeCun added, including supervised learning, reinforcement learning, as well as unsupervised or self-supervised learning.

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of robotic arms to work together faster and smarter in busy industrial settings, ...

In operation and maintenance, which accounts for the majority of research, IoT sensors and Digital Twins enable real-time monitoring of building systems. When coupled with AI, these technologies drive ...

Neural networks can better model high-level abstractions during the learning process, and combining the two techniques together has yielded state-of-the-art results across many problem areas.

Deep neural networks will move past their shortcomings without help from symbolic artificial intelligence, three pioneers of deep learning argue in a paper published in the July issue of the ...