Whether talking with banks, cell phone providers or insurance companies, customers often encounter AI-powered voice interfaces to direct their calls to the right department. But these interfaces typically are limited to understanding certain keywords. Onvego, an Israel-based startup, is working to make these systems understand what you say, no matter how you say it. Before Read article >
Anima Anandkumar is setting a personal record this week with seven of her team’s research papers accepted to NeurIPS 2020. The 34th annual Neural Information Processing Systems conference is taking place virtually from Dec. 6-12. The premier event on neural networks, NeurIPS draws thousands of the world’s best researchers every year. Anandkumar, NVIDIA’s director of Read article >
Bill Dally — one of the world’s foremost computer scientists and head of NVIDIA’s research efforts — will deliver the keynote address during GTC China, the latest event in the world’s premier conference series focused on AI, deep learning and high performance computing. Registration is not required to view the keynote, which will take place Read article >
AI is the largest technology force of our time, with the most potential to transform industries. It will bring new intelligence to healthcare, education, automotive, retail and finance, creating trillions of dollars in a new AI economy. As businesses look ahead to 2021 priorities, now’s a great time to look back at where the world Read article >
What are the fundamental laws that govern our universe? How did the matter in the universe today get there? What exactly is dark matter? The questions may be eternal, but no human scientist has an eternity to answer them. Now, thanks to NVIDIA technology and cutting-edge AI, the more than 1,000 collaborators from 26 countries Read article >
NVIDIA Research’s latest AI model is a prodigy among generative adversarial networks. Using a fraction of the study material needed by a typical GAN, it can learn skills as complex as emulating renowned painters and recreating images of cancer tissue. By applying a breakthrough neural network training technique to the popular NVIDIA StyleGAN2 model, NVIDIA Read article >
Academic researchers are developing AI to solve challenging problems with everything from agricultural robotics to autonomous flying machines. To help AI research like this make the leap from academia to commercial or government deployment, NVIDIA today announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments. The Read article >
Learn more about breakthrough NVIDIA technologies and dive into our expansive selection of graphics and simulation sessions.
Our GTC Fall 2020 virtual event featured a record breaking number of sessions, podcasts, demos, research posters, and more. We are now opening access to all the great content shared at the conference through the new NVIDIA On-Demand catalog. Learn more about breakthrough NVIDIA technologies and dive into our expansive selection of graphics and simulation sessions.
Ampere Architecture
The launch of our new Ampere architecture was a long anticipated event of this year. Designed for the age of elastic computing, it delivers the next giant leap by providing unmatched acceleration at every scale, enabling these innovators to do their life’s work. Learn about the architecture and its benefits with these sessions:
NVIDIA Ampere for Professional Workflows: Learn about these new GPUs for professional visual computing and how they provide the power of the next generation of RTX from the desktop to the data center.
Rendering at the Speed of Light on NVIDIA Ampere GPUs: Explore hardware improvements over the previous generation, best practices for application developers, and tooling improvements that will help you write high-performance graphics code.
Discover new pipelines and tools that are emerging in the graphics industry, and learn how professionals are using the newest technologies to enhance content creation.
Rendering Games With Millions of Ray-Traced Lights: Hear from NVIDIA experts to learn about the latest research in the area of many-light sampling, plus implications of many-light rendering on game content creation pipelines.
Bringing Ray-Traced Visualization to Collaborative Workflows: Omniverse XR: See how augmented reality is integrated within the Omniverse rendering pipeline, and how Omniverse AR is applied across a range of use cases. Plus, get an inside look at the different strategies created in Omniverse Kit to bring its ray tracing engine to virtual reality.
What’s New in Optix 7.2: Learn strategies to achieve optimal ray tracing performance with OptiX 7.2.
Learn about the advanced tools that help create high-quality immersive environments, and see why virtual and augmented reality are one of the most anticipated forms of content to arrive over 5G networks.
From asset creation to RTX acceleration, check out the innovative techniques that are transforming the future of graphic workflows across all industries.
Virtual Production with Cine Tracer: Hear how live action cinematographer Matt Workman has created a previsualization and virtual production app for the film industry using Unreal Engine.
Unreal Engine + RTX from a Filmmaker’s Perspective: See how today’s real-time tools with RTX power can enable indie filmmakers to tell high-concept stories without huge budgets, resources, and a big rendering farm.
Real-Time and Production Ray Tracing with V-Ray: Learn about advances in real-time ray tracing and production rendering for V-Ray workflows, including RTX acceleration and the use of CUDA, DXR, and OptiX.
To help AI research like this make the leap from academia to commercial or government deployment, NVIDIA recently announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments.
To help AI research like this make the leap from academia to commercial or government deployment, NVIDIA today announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments.
Announcing a preview release of Isaac Gym – NVIDIA’s physics simulation environment for reinforcement learning research.
For several years, NVIDIA’s research teams have been working to leverage GPU technology to accelerate reinforcement learning (RL). As a result of this promising research, NVIDIA is pleased to announce a preview release of Isaac Gym – NVIDIA’s physics simulation environment for reinforcement learning research. RL-based training is now more accessible as tasks that once required thousands of CPU cores can now instead be trained using a single GPU.
A cube manipulation task trained by Isaac Gym on a single A100 and rendered in Omniverse
RL has become one of the most promising research areas in machine learning and has demonstrated great potential for solving complex problems. RL-based systems have achieved superhuman performance in very challenging tasks, ranging from classic strategy games such as Go and Chess, to real-time computer games like StarCraft and DOTA.
Isaac Gym and NVIDIA GPUs, a reinforcement learning supercomputer
Until now, most RL robotics researchers were forced to use clusters of CPU cores for the physically accurate simulations needed to train RL algorithms. In one of the more well-known projects, the OpenAI team used almost 30,000 CPU cores (920 computers with 32 cores each) to train their robot in the Rubik’s Cube task.
In a similar task, Learning Dexterous In-Hand Manipulation, OpenAI used a cluster of 384 systems with 6144 CPU cores, plus 8 Volta V100 GPUs and required close to 30 hours of training to achieve its best results. This in-hand cube object orientation task is a challenging dexterous manipulation task, with complex physics and dynamics, many contacts, and a high-dimensional continuous control space.
Isaac Gym includes an example of this cube manipulation task for researchers to recreate the OpenAI experiment. The example supports training both recurrent and feed-forward neural networks, as well as domain randomization of physics properties that help with sim-to-real transfer. With Isaac Gym, researchers can achieve the same level of success as OpenAI’s supercomputer — on a single A100 GPU — in about 10 hours!
End to End GPU RL
Isaac Gym achieves these results by leveraging NVIDIA’s PhysX GPU-accelerated simulation engine, allowing it to gather the experience data required for robotics RL.
In addition to fast physics simulations, Isaac Gym also enables observation and reward calculations to take place on the GPU, thereby avoiding significant performance bottlenecks. In particular, costly data transfers between the GPU and the CPU are eliminated.
Implemented this way, Isaac Gym enables a complete end-to-end GPU RL pipeline.
Isaac Gym
Isaac Gym provides a basic API for creating and populating a scene with robots and objects, supporting loading data from URDF and MJCF file formats. Each environment is duplicated as many times as needed, and can be simulated simultaneously without interaction with other environments.
Isaac Gym provides a PyTorch tensor-based API to access the results of physics simulation work, allowing RL observation and reward calculations to be built using the PyTorch JIT runtime system, which dynamically compiles the python code that does these calculations into CUDA code, running on the GPU.
Observation tensors can be used as inputs to a policy inference network, and the resulting action tensors can be directly fed back into the physics system. Rollouts of observation, reward, and action buffers can stay on the GPU for the entire learning process eliminating the need to read data back from the CPU.
This set-up permits tens of thousands of simultaneous environments on a single GPU, allowing researchers to easily run experiments locally on their desktops that previously required an entire data center.
Isaac Gym also includes a basic Proximal Policy Optimization (PPO) implementation and a straightforward RL task system, but users may substitute alternative task systems or RL algorithms as desired. Also, while the included examples use PyTorch, users should also be able to integrate with TensorFlow based RL systems with some further customization.
Some additional features of Isaac Gym include:
Support for a variety of environment sensors – position, velocity, force, torque, etc.
Runtime domain randomization of physics parameters
Jacobian / inverse kinematics support
Research Results
NVIDIA’s research team has been applying Isaac Gym to a wide variety of projects. You can take a sneak-peek at some of these below, but stay tuned to https://developer.nvidia.com/blog/ for more details on these projects.
Get Started Today
Are you a researcher or academic interested in RL for robotics applications? Please download and try Isaac Gym.
Future Plans
The core functionality of Isaac Gym will be made available as part of the NVIDIA Omniverse Platform and NVIDIA’s Isaac Sim, a robotics simulation platform built on Omniverse. Until then we are making this standalone preview release available to researchers and academics to show the possibilities of end-to-end GPU-based RL and help accelerate your work in this arena.
Learn more about breakthrough NVIDIA technologies and dive into our expansive selection of graphics and simulation sessions. 
To help AI research like this make the leap from academia to commercial or government deployment, NVIDIA recently announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments.
Announcing a preview release of Isaac Gym – NVIDIA’s physics simulation environment for reinforcement learning research.