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

Scientists at UCL, Google DeepMind and Intrinsic have developed a powerful new AI algorithm that enables large sets of ...

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 ...

Lead author Matthew Lai, a PhD researcher at UCL and Google DeepMind, said, “RoboBallet transforms industrial robotics into a ...

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

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 ...

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

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.