Duration: 1:12:24

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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

𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗹𝗶𝘀𝘁:

■ AirSim Drone Racing Lab paper: https://arxiv.org/abs/2003.05654
■ Microsoft’s Autonomous Driving cookbook: https://github.com/Microsoft/AutonomousDrivingCookbook
■ AirSim @ Microsoft AI Labs: https://www.microsoft.com/en-us/ai/ai-lab-airsim-drones
■ PX4 simulation with AirSim: https://dev.px4.io/v1.9.0/en/simulation/airsim.html
■ Microsoft AirSim (GitHub): https://github.com/microsoft/airsim
■ AirSim Drone Racing Lab (Github): https://github.com/microsoft/airsim-drone-racing-lab
■ VAE and imitation learning for drone navigation​: https://github.com/microsoft/airsim-drone-racing-vae-imitation
■ Microsoft Game of Drones: (NeurIPS 2019 competition)​: https://microsoft.github.io/AirSim-NeurIPS2019-Drone-Racing
■ Drone Detection through Depth maps: https://arxiv.org/abs/1808.00259
■ Onboard detection and localization of drones using depth maps: https://ieeexplore.ieee.org/document/8984298, https://vimeo.com/277984275
■ Autonomous aerial cinematography in unstructured environments: https://www.cs.cmu.edu/~rbonatti/files/bonatti_jfr.pdf

*This on-demand webinar features a previously recorded Q&A session and open captioning.

This webinar originally aired on July 09, 2020

Explore more Microsoft Research webinars: https://aka.ms/msrwebinars