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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal>Submission Form: <a href="https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform">https://docs.google.com/forms/d/e/1FAIpQLScGCMwVm-Kzg2c3kkXd_IUNwHw8D3s06ydCg4lPgOJYkEy8aQ/viewform</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Competition Website: <a href="https://www.cs.utexas.edu/~xiao/BARN_Challenge/BARN_Challenge.html">https://www.cs.utexas.edu/~xiao/BARN_Challenge/BARN_Challenge.html</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Participation Instructions: <a href="https://github.com/Daffan/nav-competition-icra2022">https://github.com/Daffan/nav-competition-icra2022</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Dear roboticists, <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>are you interested in agile robot navigation in highly constrained spaces with a lot of obstacles around, e.g., cluttered households or after-disaster scenarios? Do you think mobile robot navigation is mostly a solved problem? Are you looking for a hands-on project for your robotics class, but may not have (sufficient) robot platforms for your students? <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>If your answer is yes to any of the above questions, we sincerely invite you to participate in our BARN challenge (<a href="https://www.cs.utexas.edu/~xiao/BARN_Challenge/BARN_Challenge.html">https://www.cs.utexas.edu/~xiao/BARN_Challenge/BARN_Challenge.html</a>)! The BARN Challenge aims at evaluating state-of-the-art autonomous navigation systems to move robots through highly constrained environments in a safe and efficient manner. The task is to navigate a standardized Clearpath Jackal robot from a predefined start to a goal location as quickly as possible without any collision. The challenge will take place both in the simulated BARN dataset and in physical obstacle courses at ICRA2022. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>1. The competition task is designing ground navigation systems to navigate through all 300 BARN environments (<a href="https://www.cs.utexas.edu/~xiao/BARN/BARN.html">https://www.cs.utexas.edu/~xiao/BARN/BARN.html</a>) and physical obstacle courses constructed at ICRA2022 as fast as possible without collision. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>2. The 300 BARN environments can be the training set for learning-based methods, or to design classical approaches in. During the simulation competition, we will generate another 50 unseen environments unavailable to the participants before the competition. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>3. We will standardize a Jackal robot in the Gazebo simulation, including a 2D Hokuyo with 720-dim 270-degree field-of-view 2D LiDAR, max speed of 2m/s, etc. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>4. Participants can use any approaches to tackle the navigation problem, such as using classical sampling-based or optimization-based planners, end-to-end learning, or hybrid approaches. We will provide baselines for reference. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>5. Standardized metrics/scoring system is provided on the website. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>6. Clearpath Robotics will provide a physical Jackal with the specified sensor and actuator at Philadelphia and we will set up physical obstacle courses in the venue. We will invite the top teams in simulation to compete in the real-world. The team who achieves the fastest navigation in the physical obstacle courses wins. <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>If you are interested in participating, please sign up at <a href="https://docs.google.com/forms/d/e/1FAIpQLSdJ6cUMHn8tQDNNkOistlpSmkS5jFt3-Xz6oh1FCMzRgxpX_g/viewform?usp=sf_link">https://docs.google.com/forms/d/e/1FAIpQLSdJ6cUMHn8tQDNNkOistlpSmkS5jFt3-Xz6oh1FCMzRgxpX_g/viewform?usp=sf_link</a> <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Co-Organizers: <o:p></o:p></p><p class=MsoNormal>Xuesu Xiao (UT Austin/Everyday Robots/GMU)<o:p></o:p></p><p class=MsoNormal>Zifan Xu (UT Austin)<o:p></o:p></p><p class=MsoNormal>Zizhao Wang (UT Austin)<o:p></o:p></p><p class=MsoNormal>Yunlong Song (University of Zurich/ETH Zurich)<o:p></o:p></p><p class=MsoNormal>Garrett Warnell (US Army Research Lab/UT Austin)<o:p></o:p></p><p class=MsoNormal>Peter Stone (UT Austin/Sony AI)<o:p></o:p></p><p class=MsoNormal>Tingnan Zhang (Robotics@Google)<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Sponsor:<o:p></o:p></p><p class=MsoNormal>Clearpath Robotics, <a href="https://clearpathrobotics.com/">https://clearpathrobotics.com/</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Thanks<o:p></o:p></p><p class=MsoNormal>Xuesu<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>-----------------------<o:p></o:p></p><p class=MsoNormal>Xuesu Xiao, Ph.D.<o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Incoming Assistant Professor (Fall 2022)<o:p></o:p></p><p class=MsoNormal>Department of Computer Science<o:p></o:p></p><p class=MsoNormal>George Mason University<o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Roboticist, The Everyday Robot Project<o:p></o:p></p><p class=MsoNormal>X, The Moonshot Factory<o:p></o:p></p><p class=MsoNormal><a href="mailto:xuesuxiao@google.com">xuesuxiao@google.com</a><o:p></o:p></p><p class=MsoNormal><a href="https://x.company/projects/everyday-robots/">https://x.company/projects/everyday-robots/</a><o:p></o:p></p><p class=MsoNormal>--<o:p></o:p></p><p class=MsoNormal>Research Affiliate<o:p></o:p></p><p class=MsoNormal>Department of Computer Science<o:p></o:p></p><p class=MsoNormal>The University of Texas at Austin<o:p></o:p></p><p class=MsoNormal><a href="mailto:xiao@cs.utexas.edu">xiao@cs.utexas.edu</a><o:p></o:p></p><p class=MsoNormal><a href="https://www.cs.utexas.edu/~xiao/">https://www.cs.utexas.edu/~xiao/</a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>