Teresa Conceicao

Enhancing AI-based Robotics with Simulation Workflows

What if you could train robust AI models for robots without real-world data? Learn how simulation generates synthetic data to close the sim-to-real gap.

Enhancing AI-based Robotics with Simulation Workflows
#1about 3 minutes

Understanding the limitations of pre-programmed robots

Pre-programmed robots often fail when environmental conditions change, highlighting the need for more adaptive AI-driven autonomy.

#2about 3 minutes

Core requirements for developing AI-powered robots

AI-based robots require massive amounts of diverse data, extensive parallel training, and rigorous testing, which are challenging to achieve in the real world.

#3about 3 minutes

An overview of NVIDIA Omniverse and Isaac Sim

NVIDIA Omniverse provides a platform for creating physically accurate virtual worlds, while Isaac Sim offers specialized tools for robotics simulation.

#4about 3 minutes

Getting started with your first robot in Isaac Sim

Learn the initial development steps by running a "Hello World" example, importing a robot model via the URDF importer, and inspecting its properties.

#5about 2 minutes

Building and collaborating on simulation environments

Leverage Omniverse's live-sync capabilities to collaborate on creating rich simulation environments by connecting to industry tools like Revit and Rhino.

#6about 4 minutes

Programming robot behavior with Python, OmniGraph, and ROS

Explore various methods for controlling robots, including direct Python APIs, the OmniGraph visual programming interface, and integration with the Robot Operating System (ROS).

#7about 3 minutes

Using synthetic data generation for AI training

Overcome the challenges of real-world data collection by using simulation to generate perfectly labeled, diverse synthetic data for training perception models.

#8about 5 minutes

Closing the sim-to-real gap with domain randomization

Mitigate the appearance and content gaps between simulation and reality by using domain randomization to create more robust and generalizable AI models.

#9about 3 minutes

Real-world examples of simulation-trained robots

See how partners like Fraunhofer, Festo, and ETH Zurich use Isaac Sim to develop, train, and test advanced robots for logistics and collaboration.

#10about 2 minutes

Resources for getting started and final Q&A

Find resources like documentation, developer forums, and conference talks to learn more, followed by a Q&A on procedural versus handmade data.

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