Nvidia upgrades the Isaac SIM robotics development platform
Nvidia on Tuesday at the Consumer Electronics Show (CES) announced significant updates to its robotics simulation tool Isaac Sim.
Isaac SDK is the first open source robotic AI development platform with simulation, navigation and manipulation. Development partners use the software tool to build and test virtual robots in realistic environments under varying operating conditions. Now accessible from the cloud, Isaac Sim is built on Nvidia Omniverse, a platform for building and managing metaverse apps.
The demand for intelligent robots is growing as more industries embrace automation to address supply chain challenges and labor shortages. The installed base of industrial and commercial robots will grow more than 6.4 times, from 3.1 million in 2020 to 20 million in 2030, according to ABI Research.
Developing, validating and deploying these new AI-based robots requires simulation technology that places them in realistic scenarios, according to Gerard Andrews, product marketing manager for Nvidia’s robotics developer community.
Isaac Sim allows roboticists to import the robot model of their choice and create realistic environments to validate a robot’s physical design and fully exercise its software stack to ensure performance. Users can generate synthetic datasets during simulation to train the robot AI models used in robot perception systems. Researchers can take advantage of the reinforcement learning API to train models in the robot’s control stack.
The latest release focuses on improving performance and functionality for manufacturing and logistics robotics use cases. The software now supports adding people and complex conveyor systems to simulation environments, and more popular assets and robots are pre-integrated to reduce time to simulation.
Robotic Operating System (ROS) developers benefit from ROS 2 Humble and Windows support. Robotics researchers are getting many new features aimed at advancing reinforcement learning, collaborative robotic programming, and robotic learning.
System enhancements focus on the needs of humans working alongside collaborative robots (cobots) or autonomous mobile robots (AMR). Isaac Sim’s new people simulation capabilities add common human-like behaviors to simulations.
For example, developers can now add human characters to simulations of a warehouse or manufacturing plant tasked with performing common behaviors like stacking packages or pushing carts. Many of the most common behaviors are already supported using a command.
To minimize the difference between the results observed in a simulated world and those observed in the real world, physically accurate sensor models are essential. Nvidia’s RTX technology allows Isaac Sim to render physically accurate data from sensors in real time. In the case of an RTX (light detection and ranging) simulated lidar, ray tracing with more speed and accurate sensor data under various lighting conditions or in response to reflective materials.
More tools for robotics researchers
Isaac Sim also provides many new simulation-ready 3D elements essential for creating physically accurate simulated environments. Everything from warehouse parts to popular robots are out of the box, so developers and users can quickly start building, according to Nvidia.
Three new features strengthen the toolset for robotics researchers:
- Isaac Gym progress reinforces learning.
- Isaac Cortex improves collaborative robot programming.
- A new tool, Isaac Orbit, provides simulation operating environments and benchmarks for learning and planning robot movements.
Isaac SIM supports warehouse conveyor and people simulation. (Image credit: Nvidia)
Expanded use of robotics underway
According to Nvidia, the robotic ecosystem already spans multiple sectors, from logistics and manufacturing to retail, energy, sustainable agriculture, and more. Its Isaac robotics platform provides advanced AI and simulation software as well as accelerated computing capabilities to the robotics ecosystem. More than a million developers and more than a thousand companies rely on one or more parts of it.
A sample of robotic operations includes:
- Telexistence deploys beverage replenishment robots in 300 convenience stores in Japan.
- To improve safety, Deutsche Bahn, Germany’s national rail company, is training AI models to handle critical but unexpected cases that rarely happen in the real world — like luggage falling onto a train track.
- Sarcos Robotics develops robots to pick up and place solar panels in renewable energy installations.
- Festo uses Isaac Cortex to simplify cobot programming and transfer simulated skills to physical robots.
- Fraunhofer develops advanced AMRs using the physically accurate and faithful visualization features of Isaac Sim.
- Flexiv uses Isaac Replicator for generating synthetic data to train AI models.