You are viewing content from a past/completed QCon

Presentation: From Robot Simulation to the Real-World

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 10:00am - 10:40am

Day of week: Wednesday

Share this on:

Abstract

Simulation is one of the most powerful tools in the robot developer's tool belt. Besides allowing quicker, safer and cheaper iterations, it can be used to prototype before building, run continuous integration, train machine learning algorithms, etc. One popular open source robotics simulator is Gazebo, maintained by Open Robotics, the same foundation that maintains ROS, the Robot Operating System. Combined, ROS and Gazebo are used by an increasing number of developers around the world.

 

Gazebo's development started over 17 years ago, but its most current form started taking shape in 2012, when DARPA sponsored Open Robotics to run the Virtual Robotics Challenge, the first stage of its Robotics Challenge. A total of 26 teams competed in the virtual competition, controlling an Atlas robot from Boston Dynamics in a simulated disaster scenario. As a result of the virtual competition, the top 7 teams received funding to compete in the final competition with the same robot, but this time a physical one in a physical scenario.

 

Ever since, Gazebo has continued to be developed and improved to better support various types of robots, spanning ground, water and air, and is being increasingly used by the academia, industries and, sure enough, in other competitions. In this talk, Louise will give an overview of Gazebo's architecture and go over some examples of projects using Gazebo which Open Robotics has been involved with, describing how they bridged virtual robots to their physical counterparts.

Speaker: Louise Poubel

Software Engineer @OpenRoboticsOrg

 

Louise Poubel is a software engineer at Open Robotics working on free and open source tools for robotics, like the robot simulator Gazebo and the Robot Operating System (ROS). Louise first got involved with Open Robotics through GNOME’s Outreach Program for Women. Louise grew up in Brazil and went to college in Japan, where she received her BS in electromechanical engineering from Chiba University. She also holds a joint MEng in advanced robotics from École Centrale de Nantes and Warsaw University of Technology, where her research focused on real-time, whole-body human motion imitation by humanoid robots.

Find Louise Poubel at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks