Harnessing high-fidelity simulation for autonomous systems through AirSim

Robots and autonomous systems are playing a significant role in modern times, in both academic research and industrial applications. Handling the constant variability and uncertainty present in the real world is a major challenge for autonomous systems as their areas of usage expand. Recently, machine learning techniques, such as deep neural networks, have shown promise as building blocks for improving robot intelligence, and high visual and physical fidelity simulation has the potential to address the needs of data-driven autonomy algorithms.

In this webinar, Sai Vemprala, a Microsoft researcher, will introduce Microsoft AirSim, an open-source, high-fidelity robotics simulator, and he demonstrates how it can help to train robust and generalizable algorithms for autonomy. He will explain the features of Microsoft AirSim while giving an overview of some research projects that have benefited from AirSim, particularly focusing on robotics and how these algorithms are trained with simulated data but are capable of working in real life. He will also introduce AirSim Drone Racing Lab, an enhancement of AirSim aimed at enabling robotics and machine learning researchers to tackle the specific domain of autonomous drone racing.

Together, you’ll explore:

How simulation can address the needs of data-driven autonomy algorithms
General features and usage of Microsoft AirSim
How robotics research projects have employed AirSim for training AI models capable of sim-to-real transfer
How you can get started with the AirSim Drone Racing Lab and use it to generate data for perception, planning, and control algorithms for autonomous drones

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