Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks.
Robótica | 🇬🇧 Robotics
The TIMESTORM consortium, funded by the EU’s Future and Emerging Technologies (FET) programme, has transformed the notion of time perception in artificial intelligence from an immature, poorly defined subject into a promising new research strand, drawing on diverse expertise in psychology and neurosciences as well as robotics and cognitive systems.